Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s "gist" and Lowe’s SIFT descriptors.
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The full-image view is methodologically consistent with traditional methods where encoded SIFT vectors of the entire image are pooled together.
“Second, the full-image view is methodologically consistent with the traditional methods [15], [17], [19] where the encoded SIFT vectors of the entire image are pooled together.”
From Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition (2015) · cites this paper as [15]
Cites
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337 · see citing works
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Oliva, A., & Torralba, A. B. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,321 · see citing works
Swain, M. J., & Ballard, D. H. (1991) · International Journal of Computer Vision · doi:10.1007/bf00130487 · Cited by 5,625 · see citing works
A Bayesian Hierarchical Model for Learning Natural Scene Categories
Fei-Fei, L., & Perona, P. (2005) · 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.2005.16 · Cited by 3,657 · see citing works
(2000) · Applied Physics Letters · doi:10.1162/jmlr.2003.3.4-5.993 · Cited by 3,030 · see citing works
Unsupervised Learning by Probabilistic Latent Semantic Analysis
Hofmann, T. (2001) · Machine Learning · doi:10.1023/a:1007617005950 · Cited by 2,455 · see citing works
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 · see citing works
Object class recognition by unsupervised scale-invariant learning
Fergus, R., Perona, P., & Zisserman, A. (2003) · 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.2003.1211479 · Cited by 2,030 · see citing works
The pyramid match kernel: discriminative classification with sets of image features
Grauman, K. L., & Darrell, T. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.239 · Cited by 1,500 · see citing works
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
Zhang, H., Berg, A. C., Maire, M., & Malik, J. (2006) · 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.2006.301 · Cited by 1,128 · see citing works
Discovering objects and their location in images
Šivic, J., Russell, B. C., Efros, A. A., Zisserman, A., & Freeman, W. T. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.77 · Cited by 973 · see citing works
Context-based vision system for place and object recognition
Torralba, Murphy, M., Freeman, & Рубин (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238354 · Cited by 867 · see citing works
Shape Matching and Object Recognition Using Low Distortion Correspondences
Berg, A. C., Berg, T. L., & Malik, J. (2005) · 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.2005.320 · Cited by 850 · see citing works
Indoor-outdoor image classification
Szummer, M., & Picard, R. W. (2002) · doi:10.1109/caivd.1998.646032 · Cited by 641 · see citing works
Recognition without Correspondence using Multidimensional Receptive Field Histograms
Schiele, B., & Crowley, J. L. (2000) · International Journal of Computer Vision · doi:10.1023/a:1008120406972 · Cited by 394 · see citing works
Recognition with local features: the kernel recipe
Wallraven, C., Barbara Caputo, B., & Graf (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238351 · Cited by 348 · see citing works
Modeling scenes with local descriptors and latent aspects
Quelhas, P., Monay, F., Odobez, J., Gática-Pérez, D., Tuytelaars, T., & Gool, L. V. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.152 · Cited by 341 · see citing works
Weak Hypotheses and Boosting for Generic Object Detection and Recognition
Opelt, A., Fussenegger, M., Pinz, A. J., & Auer, P. L. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24671-8_6 · Cited by 266 · see citing works
Multiresolution histograms and their use for recognition
Hadjidemetriou, E., Grossberg, M. D., & Nayar, S. K. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.32 · Cited by 253 · see citing works
Biologically Motivated Computer Vision
Bülthoff, H., Poggio, T., Lee, S.-W., & Wallraven, C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36181-2 · Cited by 223 · see citing works
A maximum entropy framework for part-based texture and object recognition
Lazebnik, S., Schmid, C., & Ponce, J. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.10 · Cited by 192 · see citing works
Self-citationContent-based query of image databases: inspirations from text retrieval
Squire, D., Müller, W., Müller, H., & Pun, T. (2000) · Pattern Recognition Letters · doi:10.1016/s0167-8655(00)00081-7 · Cited by 191 · see citing works
Texture orientation for sorting photos "at a glance"
Gorkani, M., & Picard, R. W. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1994.576325 · Cited by 187 · see citing works
The Structure of Locally Orderless Images
Koenderink, J. J., & van Doorn, A. J. (1999) · International Journal of Computer Vision · doi:10.1023/a:1008065931878 · Cited by 162 · see citing works
Squire, D., Müller, W., Müller, H., & Raki, J. (1998) · Archive ouverte UNIGE (University of Geneva) · Cited by 127 · see citing works
Bidirectional Reflection Distribution Function of Thoroughly Pitted Surfaces
Koenderink, J. J., van Doorn, A. J., Dana, K., & Nayar, S. K. (1999) · International Journal of Computer Vision · doi:10.1023/a:1008061730969 · Cited by 120 · see citing works
Categorizing Nine Visual Classes Using Local Appearance Descriptors
Willamowski, J. K., Arregui, D., Csurka, G., Dance, C. R., & Fan, L. (2004) · Cited by 112 · see citing works
Local Features and Kernels for Classification of Texture and Object Categories: An In-Depth Study
Zhang, J., Marszałek, M., Lazebnik, S., & Schmid, C. (2005) · HAL · Cited by 105 · see citing works
Self-citationTowards a Computational Model for Object Recognition in IT Cortex
Lowe, D. G. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45482-9_3 · Cited by 75 · see citing works
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438 · see citing works
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054 · see citing works
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Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
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He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971 · see citing works
Learning Deep Features for Discriminative Localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428 · see citing works
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307 · see citing works
Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513 · see citing works
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,222 · see citing works
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Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980 · see citing works
Object Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545 · see citing works
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Krause, J., Stark, M. M., Deng, J., & Fei-Fei, L. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.77 · Cited by 3,523 · see citing works
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Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · 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.2008.4587756 · Cited by 3,481 · see citing works
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Nilsback, M.-E., & Zisserman, A. (2008) · 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing · doi:10.1109/icvgip.2008.47 · Cited by 3,442 · see citing works
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Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150 · see citing works
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He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034 · see citing works
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Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100 · see citing works
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Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412 · see citing works
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification
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Alahi, A., Ortiz, R., & Vandergheynst, P. (2012) · 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.2012.6247715 · Cited by 1,791 · see citing works
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Zhang, H., Berg, A. C., Maire, M., & Malik, J. (2006) · 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.2006.301 · Cited by 1,128 · see citing works
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Perronnin, F., Sánchez, J., & Mensink, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_11 · Cited by 2,588 · see citing works
The role of context in object recognition
Oliva, A., & Torralba, A. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.09.009 · Cited by 1,063 · see citing works
Multiple object tracking: A literature review
Luo, W., Xing, J., Milan, A., Zhang, X.-Q., Liu, W., Zhao, X., & Kim, T.-K. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2020.103448 · Cited by 1,095 · see citing works
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
Ranzato, M., Huang, F., Boureau, Y.-L., & LeCun, Y. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383157 · Cited by 1,186 · see citing works
A Survey of Sparse Representation: Algorithms and Applications
Zhang, Z., Xu, Y. D., Yang, J. J., Li, X., & Zhang, D. D. (2015) · IEEE Access · doi:10.1109/access.2015.2430359 · Cited by 1,088 · see citing works
In defense of Nearest-Neighbor based image classification
Boiman, O., Shechtman, E., & Irani, M. (2008) · 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.2008.4587598 · Cited by 1,079 · see citing works
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Liu, C., Chen, L.-C., Schroff, F., Adam, H., Hua, W., Yuille, A., & Fei-Fei, L. (2019) · 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.2019.00017 · Cited by 1,076 · see citing works
Learning mid-level features for recognition
Boureau, Y.-L., Bach, F., LeCun, Y., & Ponce, 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.2010.5539963 · Cited by 1,063 · see citing works
Self-citationMulti-scale Orderless Pooling of Deep Convolutional Activation Features
Gong, Y., Wang, L., Guo, R., & Lazebnik, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_26 · Cited by 1,043 · see citing works
Self-citationSemantic texton forests for image categorization and segmentation
Shotton, J., Johnson, M., & Cipolla, R. (2008) · 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.2008.4587503 · Cited by 1,027 · see citing works
Shotton, J., Winn, J. M., Rother, C., & Criminisi, A. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0109-1 · Cited by 1,030 · see citing works
Anomaly Detection and Localization in Crowded Scenes
Li, W., Mahadevan, V., & Vasconcelos, N. M. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.111 · Cited by 1,024 · see citing works
ParseNet: Looking Wider to See BetterPreprint
Liu, W., Rabinovich, A., & Berg, A. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.04579 · Cited by 1,056 · see citing works
Cheng, G., Han, J., Zhou, P., & Guo, L. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.10.002 · Cited by 1,007 · see citing works
Cheng, G., Xie, X., Han, J., Guo, L., & Xia, G.-S. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3005403 · Cited by 1,012 · see citing works
Classification using intersection kernel support vector machines is efficient
Maji, S., Berg, A. C., & Malik, J. (2008) · 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.2008.4587630 · Cited by 946 · see citing works
An Analysis of Scale Invariance in Object Detection - SNIP
Singh, B., & Davis, L. S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00377 · Cited by 913 · see citing works
Temporal Action Detection with Structured Segment Networks
Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., & Lin, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.317 · Cited by 892 · see citing works
Multimodal Distributional Semantics
Bruni, E., Tran, N. K., & Baroni, M. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4135 · Cited by 910 · see citing works
Gao, Y., Beijbom, O., Zhang, N., & Darrell, T. (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.2016.41 · Cited by 912 · see citing works
Towards better exploiting convolutional neural networks for remote sensing scene classification
Nogueira, K., Penatti, O. A. B., & dos Santos, J. A. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.001 · Cited by 909 · see citing works
Data-Driven Grasp Synthesis—A Survey
Bohg, J., Morales, A., Asfour, T., & Kragic, D. (2014) · IEEE Transactions on Robotics · doi:10.1109/tro.2013.2289018 · Cited by 929 · see citing works
Im2Text: Describing Images Using 1 Million Captioned Photographs
Ordóñez, V., Kulkarni, G. A., & Berg, T. L. (2011) · Cited by 879 · see citing works
SUN attribute database: Discovering, annotating, and recognizing scene attributes
Patterson, G., & Hays, J. H. (2012) · 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.2012.6247998 · Cited by 878 · see citing works
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing
Hou, Q., Li, Z., Cheng, M., & Feng, J. (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/cvpr42600.2020.00406 · Cited by 876 · see citing works
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition
Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q. (2015) · PLOS ONE · doi:10.1371/journal.pone.0140381 · Cited by 879 · see citing works
“Among those methods, the most notable work is SPM [15], which splits the image into hierarchical cells, computes BoW representation for each cell, and finally weights and concatenates the results.”
BackgroundMethodInfluentialEitz, M., Hays, J. H., & Alexa, M. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185540 · Cited by 871 · see citing works
- Authors
- Lazebnik, S., Schmid, C., & Ponce, J.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Centre Inria de l'Université Grenoble Alpes (FR)
- ORCID
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 2
- Pages
- 2169-2178
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2162915993
- Cited by
- 7,916
- References
- 30
- Citations by year
- 2026: 34 · 2025: 92 · 2024: 118 · 2023: 155 · 2022: 190 · 2021: 266 · 2020: 334 · 2019: 406 · 2018: 493 · 2017: 600 · 2016: 746 · 2015: 872 · 2014: 924 · 2013: 743 · 2012: 541
- FWCI
- 149.57
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- other-oa
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- scene classification, spatial pyramid matching, bag of visual words, geometric correspondence, image segmentation, Caltech-101, gastrointestinal stromal tumor, SIFT descriptors, image representation
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.
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