Cited by 3,442Image Retrieval and Classification TechniquesAdvanced Image and Video Retrieval TechniquesAdvanced Neural Network Applications

The DOI matches its Crossref record; 18 of the 20 references it cites were found in a registry or an index.

Automated Flower Classification over a Large Number of Classes

Nilsback, Maria-Elena, Zisserman, Andrew·2008·2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing

DOI 10.1109/icvgip.2008.47

Open this paper in CoChat — free account

Abstract

We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower dataset. We compute four different features for the flowers, each describing different aspects, namely the local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour. We combine the features using a multiple kernel framework with a SVM classifier. The weights for each class are learnt using the method of Varma and Ray, which has achieved state of the art performance on other large dataset, such as Caltech 101/256. Our dataset has a similar challenge in the number of classes, but with the added difficulty of large between class similarity and small within class similarity. Results show that learning the optimum kernel combination of multiple features vastly improves the performance, from 55.1% for the best single feature to 72.8% for the combination of all features.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1109/icvgip.2008.47

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Learning with Kernels
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
Boykov, Y. Y., & Jolly, M.-P. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937505 · Cited by 3,674
Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., & Perona, P. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.09.012 · Cited by 2,359
Untitled work
(2007) · CaltechAUTHORS (California Institute of Technology) · Cited by 2,274
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study
Zhang, J., Marszałek, M., Lazebnik, S., & Schmid, C. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-9794-4 · Cited by 1,962
Multiple kernel learning, conic duality, and the SMO algorithm
Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015424 · Cited by 1,453
Representing shape with a spatial pyramid kernel
Bosch, A., Zisserman, A., & Muñoz, X. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282340 · Cited by 1,423
Self-citation

Show all 20 references

Sorted by citations

Items 501–600 of 1,007

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view LearningPreprint
Minh, H. Q., Bazzani, L., & Murino, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.8066 · Cited by 49
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer LearningPreprint
Whatmough, P. N., Zhou, C., Hansen, P. R., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.11128 · Cited by 49
Feature Combination and the kNN Framework in Object Classification
Hou, J., Gao, H., Xia, Q., & Qi, N. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2461552 · Cited by 48
Multi-Scale Grid Network for Image Deblurring With High-Frequency Guidance
Liu, Y., Fang, F., Wang, T., Li, J., Sheng, Y., & Zhang, G. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3090206 · Cited by 50
Private-Shared Disentangled Multimodal VAE for Learning of Latent Representations
Lee, M., & Pavlovic, V. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00185 · Cited by 59
Image synthesis: a review of methods, datasets, evaluation metrics, and future outlook
Baraheem, S. S., Le, T.-N., & Nguyen, T. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10434-2 · Cited by 52
Recent advances of few-shot learning methods and applications
Wang, J., Liu, K., Zhang, Y., Leng, B., & Lü, J. (2023) · Science China Technological Sciences · doi:10.1007/s11431-022-2133-1 · Cited by 50
ReViT: Enhancing vision transformers feature diversity with attention residual connections
Diko, A., Avola, D., Cascio, M., & Cinque, L. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110853 · Cited by 54
A simple feature combination method based on dominant sets
Hou, J., & Pelillo, M. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.04.005 · Cited by 48
Classification-Specific Parts for Improving Fine-Grained Visual Categorization
Korsch, D., Bodesheim, P., & Denzler, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33676-9_5 · Cited by 45
Distinguishing Unseen from Seen for Generalized Zero-shot Learning
Su, H., Li, J., Chen, Z., Zhu, L., & Lü, K. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00773 · Cited by 47
1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions
Yin, D., Yang, Y., Wang, Z., Yu, H., Wei, K., & Sun, X. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01926 · Cited by 52
InterActive: Inter-Layer Activeness Propagation
Xie, L., Zheng, L., Wang, J., Yuille, A., & Tian, Q. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.36 · Cited by 48
Multiview Label Sharing for Visual Representations and Classifications
Zhang, C., Cheng, J., & Tian, Q. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2759500 · Cited by 47
Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy
Scheidegger, F., Istrate, R., Mariani, G., Benini, L., Bekas, C., & Malossi, C. (2020) · The Visual Computer · doi:10.1007/s00371-020-01922-5 · Cited by 52
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition
Kozerawski, J., & Turk, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00363 · Cited by 47
Semi-Supervised Learning for Few-Shot Image-to-Image Translation
Wang, Y., Khan, S. H., González-García, A., van de Weijer, J., & Khan, F. S. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00451 · Cited by 48
Investigating the Bilateral Connections in Generative Zero-Shot Learning
Li, J., Jing, M., Lü, K., Zhu, L., & Shen, H. T. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3050803 · Cited by 46
Meta-Learning for Few-Shot Plant Disease Detection
Chen, L., Cui, X., & Li, W. (2021) · Foods · doi:10.3390/foods10102441 · Cited by 51
Concept Learners for Few-Shot LearningPreprint
Cao, K., Brbić, M., & Leskovec, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.07375 · Cited by 52
Classification of flower image based on attention mechanism and multi-loss attention network
Zhang, M., Su, H., & Wen, J. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.09.001 · Cited by 46
Two-Way Multi-Label Loss
Kobayashi, T. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00722 · Cited by 49
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Xu, H., Zhi, S., Sun, S., Patel, V. M., & Liu, L. (2025) · ACM Computing Surveys · doi:10.1145/3718362 · Cited by 57
Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data
Yan, X., Acuna, D., & Fidler, S. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00395 · Cited by 48
Faces à la Carte: Text-to-Face Generation via Attribute Disentanglement
Wang, T., Zhang, T., & Lovell, B. C. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00342 · Cited by 49
Dual VAEGAN: A generative model for generalized zero-shot learning
Luo, Y., Wang, X., & Pourpanah, F. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107352 · Cited by 46
Incremental Zero-Shot Learning
Wei, K., Deng, C. D., Yang, X., & Tao, D. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3110369 · Cited by 46
Multi-caption Text-to-Face Synthesis: Dataset and Algorithm
Sun, J.-X., Li, Q., Wang, W.-N., Zhao, J., & Sun, Z.-N. (2021) · ACM Multimedia · doi:10.1145/3474085.3475391 · Cited by 64
ISD: Self-Supervised Learning by Iterative Similarity Distillation
Tejankar, A., Koohpayegani, S. A., Pillai, V., Favaro, P., & Pirsiavash, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00947 · Cited by 45
Distribution-Aware Prompt Tuning for Vision-Language Models
Cho, E., Kim, J., & Kim, H. J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02011 · Cited by 50
SoftCLIP: Softer Cross-Modal Alignment Makes CLIP Stronger
Gao, Y., Liu, J., Xu, Z., Wu, T., Zhang, E., Li, K., Yang, J., Liu, W., & Sun, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27955 · Cited by 65
Progressive Visual Prompt Learning with Contrastive Feature Re-formation
Chen, X., Zhu, Y., Shen, H., Chen, B., Liao, Y., Chen, X., & Wang, L. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02172-x · Cited by 55
ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models
Tian, X., Zou, S., Yang, Z., & Zhang, J. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02700 · Cited by 54
ℓp norm multiple kernel Fisher discriminant analysis for object and image categorisation
Yan, F., Mikolajczyk, K., Barnard, M., Cai, H., & Kittler, J. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539916 · Cited by 45
End-to-end cross-modality retrieval with CCA projections and pairwise ranking loss
Dorfer, M., Schlüter, J., Vall, A., Korzeniowski, F., & Widmer, G. (2018) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-018-0151-5 · Cited by 52
Adding New Tasks to a Single Network with Weight Transformations Using Binary Masks
Mancini, M., Ricci, E., Barbara Caputo, B., & Bulò, S. R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_14 · Cited by 45
Are open set classification methods effective on large-scale datasets?
Roady, R., Hayes, T. L., Kemker, R., Gonzales, A., & Kanan, C. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238302 · Cited by 49
A review of design intelligence: progress, problems, and challenges
Tang, Y., Huang, J., Yao, M.-T., Wei, J., Li, W., He, Y., & Li, Z. (2019) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1900398 · Cited by 44
AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES
Ghosh, B., Garg, S., & Motagh, M. (2022) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-3-2022-201-2022 · Cited by 48
Non-sparse multiple kernel fisher discriminant analysis
Yan, F., Kittler, J., Mikolajczyk, K., & Tahir, A. (2012) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 45
On Improving Adversarial Transferability of Vision TransformersPreprint
Naseer, M., Ranasinghe, K., Khan, S. H., Khan, F. S., & Porikli, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04169 · Cited by 75
Generative Multi-Label Zero-Shot Learning
Gupta, A., Narayan, S., Khan, S. H., Khan, F. S., Shao, L., & van de Weijer, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3295772 · Cited by 57
Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views
Zhang, J., & Ma, K. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01615 · Cited by 45
Dynamic Unary Convolution in Transformers
Duan, H., Long, Y., Wang, S., Zhang, H., Willcocks, C. G., & Shao, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3233482 · Cited by 45
ALIP: Adaptive Language-Image Pre-training with Synthetic Caption
Yang, K., Deng, J., An, X., Li, J., Feng, Z., Guo, J., Yang, J., & Liu, T. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00273 · Cited by 50
Perceptual Pyramid Adversarial Networks for Text-to-Image Synthesis
Gao, L., Chen, D., Song, J., Xu, X., Zhang, D., & Shen, H. T. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018312 · Cited by 44
Ranking Neural Checkpoints
Li, Y., Jia, X., Sang, R., Zhu, Y., Green, B., Wang, L., & Gong, B. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00269 · Cited by 52
The Impact of Color on Bag-of-Words Based Object Recognition
Vigo, D. A. R., Khan, F. S., van de Weijer, J., & Gevers, T. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.383 · Cited by 44
Sonar data classification by using few-shot learning and concept extraction
Ghavidel, M., Azhdari, S. M. H., Khishe, M., et al. (2022) · Applied Acoustics · doi:10.1016/j.apacoust.2022.108856 · Cited by 43
Self-reconstruction network for fine-grained few-shot classification
Li, X., Li, Z., Xie, J., et al. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110485 · Cited by 43
Enhancing performance of vision transformers on small datasets through local inductive bias incorporation
Akkaya, İ. B., Kathiresan, S. S., Arani, E., & Zonooz, B. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110510 · Cited by 50
Application of deep learning to computer vision: A comprehensive study
Islam, S. M. S., Rahman, S., Rahman, M. M., Dey, E. K., & Shoyaib, M. (2016) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/iciev.2016.7760071 · Cited by 43
Sparse Deep Transfer Learning for Convolutional Neural Network
Liu, J., Wang, Y., & Qiao, Y. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10801 · Cited by 45
Medicinal plant leaf information extraction using deep features
Prasad, S., & Singh, P. P. (2017) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2017.8228324 · Cited by 45
ELPephants: A Fine-Grained Dataset for Elephant Re-Identification
Körschens, M., & Denzler, J. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00035 · Cited by 45
Scalable Online Convolutional Sparse Coding
Wang, Y., Yao, Q., Kwok, J. T.-Y., & Ni, L. M.-S. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2842152 · Cited by 43
Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs)
Liu, W., Juhas, M., & Zhang, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.547327 · Cited by 43
Variable Sparsity Kernel Learning
Aflalo, J., Ben‐Tal, A., Bhattacharyya, C., et al. (2011) · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · doi:10.5555/1953048.1953065 · Cited by 43
Terminal brain damage: exposing the graceless degradation in deep neural networks under hardware fault attacksPreprint
Hong, S., Frigo, P., Kaya, Y., Giuffrida, C., & Dumitraş, T. (2019) · arXiv (Cornell University) · doi:10.5555/3361338.3361373 · Cited by 50
A survey on multimodal bidirectional machine learning translation of image and natural language processing
Nam, W. K., & Jang, B. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121168 · Cited by 49
Hierarchical Prompt Learning for Multi-Task Learning
Liu, Y., Lu, Y., Liu, H., An, Y., Xu, Z., Yao, Z., Zhang, B., Xiong, Z., & Gui, C. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01048 · Cited by 44
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models
Kan, B., Wang, T., Lü, W., Zhen, X., Guan, W., & Zheng, F. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01436 · Cited by 48
Semi-Supervised Learning for Fine-Grained Classification With Self-Training
Nartey, O. T., Yang, G., Wu, J., & Asare, S. K. (2019) · IEEE Access · doi:10.1109/access.2019.2962258 · Cited by 43
Flower classification with modified multimodal convolutional neural networks
Bae, K. I., Park, J., Lee, J., Lee, Y.-S., & Lim, C. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113455 · Cited by 43
Danish Fungi 2020 – Not Just Another Image Recognition Dataset
Picek, L., Šulc, M., Matas, J., Jeppesen, T. S., Heilmann‐Clausen, J., Lassøe, T., & Frøslev, T. G. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00334 · Cited by 47
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning
Xu, H., Zhang, X., Li, H., Xie, L., Dai, W., Xiong, H., & Tian, Q. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3176690 · Cited by 47
Four Things Everyone Should Know to Improve Batch NormalizationPreprint
Summers, C., & Dinneen, M. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03548 · Cited by 42
Flower image segmentation with PCA fused colored covariance and gabor texture features based level sets
Inthiyaz, S., Madhav, B. T. P., & Kishore, P. V. V. (2018) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2017.12.007 · Cited by 41
iColoriT: Towards Propagating Local Hints to the Right Region in Interactive Colorization by Leveraging Vision Transformer
Yun, J., Lee, S., Park, M., & Choo, J. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00183 · Cited by 44
On-device Training: A First Overview on Existing Systems
Zhu, S., Voigt, T., Rahimian, F., & Ko, J. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3696003 · Cited by 66
Enhancing few-shot image classification through learnable multi-scale embedding and attention mechanisms
Askari, F., Fateh, A., & Mohammadi, M. R. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107339 · Cited by 44
An automatic flower classification approach using machine learning algorithms
Zawbaa, H. M., Abbass, M., Basha, S. H., et al. (2014) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2014.6968612 · Cited by 41
Image Classification Using Multiscale Information Fusion Based on Saliency Driven Nonlinear Diffusion Filtering
Hu, W., Hu, R., Xie, N., Ling, H., & Maybank, S. J. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2303639 · Cited by 41
Top-down saliency detection driven by visual classification
Murabito, F., Spampinato, C., Palazzo, S., et al. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.03.005 · Cited by 41
Citizen Science and Climate Change: Mapping the Range Expansions of Native and Exotic Plants with the Mobile App Leafsnap
Kress, W. J., García‐Robledo, C., Soares, J. V. B., Jacobs, D. W., Wilson, K., Lopez, I. C., & Belhumeur, P. N. (2018) · BioScience · doi:10.1093/biosci/biy019 · Cited by 45
Text-to-image Synthesis via Symmetrical Distillation Networks
Yuan, M., & Peng, Y. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240559 · Cited by 40
Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability
Amirian, M., & Schwenker, F. (2020) · IEEE Access · doi:10.1109/access.2020.3007337 · Cited by 49
Flower Image Classification Using Deep Convolutional Neural Network
Alipour, N., Tarkhaneh, O., Awrangjeb, M., et al. (2021) · International Conference on Web Research (ICWR) · doi:10.1109/icwr51868.2021.9443129 · Cited by 41
Multi-scale Sparse Network with Cross-Attention Mechanism for image-based butterflies fine-grained classification
Li, M., Zhou, G., Cai, W., Li, J., Li, M., He, M., Hu, Y., & Li, L. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108419 · Cited by 40
When do GANs replicate? On the choice of dataset size
Feng, Q., Guo, C., Benitez-Quiroz, F., & Martı́nez, A. M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00663 · Cited by 41
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman KernelsPreprint
Ru, B., Wan, X., Dong, X., & Osborne, M. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07556 · Cited by 41
Relaxed Block-Diagonal Dictionary Pair Learning With Locality Constraint for Image Recognition
Chen, Z., Wu, X., & Kittler, J. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3053941 · Cited by 40
Deep Vision Multimodal Learning: Methodology, Benchmark, and Trend
Chai, W., & Wang, G. (2022) · Applied Sciences · doi:10.3390/app12136588 · Cited by 48
Bi-Level Meta-Learning for Few-Shot Domain Generalization
Qin, X., Song, X., & Jiang, S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01526 · Cited by 40
Global and Local Prompts Cooperation via Optimal Transport for Federated Learning
Li, H., Huang, W. X., Wang, J., & Shi, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01155 · Cited by 52
Plant identification: man vs. machine
Bonnet, P., Joly, A., Goëau, H., et al. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2607-4 · Cited by 40
Linear Dependency Modeling for Classifier Fusion and Feature Combination
Ma, A. J., Yuen, P. C., & Lai, J. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.198 · Cited by 41
Layout2image: Image Generation from Layout
Zhao, B., Yin, W., Meng, L., & Sigal, L. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01300-7 · Cited by 41
A new dataset of dog breed images and a benchmark for finegrained classification
Zou, D.-N., Zhang, S., Mu, T., & Zhang, M. (2020) · Computational Visual Media · doi:10.1007/s41095-020-0184-6 · Cited by 44
Distilling Global and Local Logits with Densely Connected Relations
Kim, Y., Park, J., Jang, Y., Ali, M. S., Oh, T.-H., & Bae, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00623 · Cited by 43
An Erudite Fine-Grained Visual Classification Model
Chang, D., Tong, Y., Du, R., Hospedales, T. M., Song, Y.-Z., & Ma, Z. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00702 · Cited by 42
Learning adversarial semantic embeddings for zero-shot recognition in open worlds
Li, T., Pang, G., Bai, X. P., Zheng, J. G., Zhou, L., & Ning, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110258 · Cited by 39
Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model
Wang, Z., Wei, L., Wang, T., Chen, H., Hao, Y., Wang, X., He, X., & Tian, Q. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01630 · Cited by 52
Stacking VAE and GAN for Context-aware Text-to-Image Generation
Zhang, C., & Peng, Y. (2018) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2018.8499439 · Cited by 39
Attribute Group Editing for Reliable Few-shot Image Generation
Ding, G., Han, X., Wang, S., Wu, S., Jin, X., Tu, D., & Huang, Q. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01091 · Cited by 39
K for the Price of 1: Parameter-efficient Multi-task and Transfer LearningPreprint
Mudrakarta, P. K., Sandler, M., Zhmoginov, A., & Howard, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10703 · Cited by 56
Training GANs with Stronger Augmentations via Contrastive DiscriminatorPreprint
Jeong, J., & Shin, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.09742 · Cited by 39
Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning
Denize, J., Rabarisoa, J., Orcesi, A., Hérault, R., & Canu, S. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00273 · Cited by 41
Learning to Compare Relation: Semantic Alignment for Few-Shot Learning
Cao, C., & Zhang, Y. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3142530 · Cited by 38
On the importance of integrating convolution features for Indian medicinal plant species classification using hierarchical machine learning approach
Pushpa, B. R., Rani, N. S., Chandrajith, M., et al. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102611 · Cited by 38

Authors: Nilsback, M.-E., & Zisserman, A.
Affiliations: University of Oxford (GB)
ORCID: Zisserman, Andrew
Venue: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Publisher: IEEE
Published: 2008-12
Pages: 722-729
DOI: 10.1109/icvgip.2008.47
OpenAlex: W2533598788
Semantic Scholar: 02b28f3b71138a06e40dbd614abf8568420ae183
Cited by: 3,442
References: 20
Citations by year: 2026: 304 · 2025: 516 · 2024: 502 · 2023: 447 · 2022: 316 · 2021: 349 · 2020: 223 · 2019: 212 · 2018: 125 · 2017: 96 · 2016: 93 · 2015: 70 · 2014: 56 · 2013: 41 · 2012: 34
FWCI: 15.48
Type: conference paper
Language: en
Open access: closed
Locations: Indian Conference on Computer Vision, Graphics and Image Processing (publishedVersion)
Funders: Microsoft Research
Royal Academy of Engineering
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: flower classification, large number of classes, feature combination, multiple kernel learning, support vector machine, color features

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Automated Flower Classification over a Large Number of Classes

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks