The DOI matches its Crossref record; 18 of the 19 references it cites were found in a registry or an index.
PCA-SIFT: a more distinctive representation for local image descriptors
Abstract
Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid (June 2003) recently evaluated a variety of approaches and identified the SIFT [D. G. Lowe, 1999] algorithm as being the most resistant to common image deformations. This paper examines (and improves upon) the local image descriptor used by SIFT. Like SIFT, our descriptors encode the salient aspects of the image gradient in the feature point's neighborhood; however, instead of using SIFT's smoothed weighted histograms, we apply principal components analysis (PCA) to the normalized gradient patch. Our experiments demonstrate that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation. We also present results showing that using these descriptors in an image retrieval application results in increased accuracy and faster matching.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
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
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 14,137
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Goodall, C. R., & Jolliffe, I. T. (1988) · Technometrics · doi:10.2307/1270093 · Cited by 8,797
Jolliffe, I. T. (1986) · Springer series in statistics · doi:10.1007/978-1-4757-1904-8 · Cited by 8,481
Face recognition using eigenfaces
Turk, M., & Pentland, A. (2002) · 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.1991.139758 · Cited by 5,262
The design and use of steerable filters
Freeman, W. T., & Adelson, E. H. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.93808 · Cited by 2,937
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
Indexing based on scale invariant interest points
Mikolajczyk, K., & Schmid, C. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937561 · Cited by 1,101
Flagged first, then by citations
1 of the 19 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- schaffalitzky, 2002, Multi-view matching for unordered image sets, Proceedings of European Conference on Computer Vision, 1, 414
- 0, Viewpoint change sequences
Cited by
Sorted by citations
Items 301–400 of 1,000
Hand gesture recognition using Bag-of-features and multi-class Support Vector Machine
Dardas, N. H., Chen, Q., Georganas, N. D., & Petriu, E. M. (2010) · IEEE International Workshop on Haptic Audio visual Environments and Games · doi:10.1109/have.2010.5623982 · Cited by 59
A Novel Image Registration Algorithm for Remote Sensing Under Affine Transformation
Song, Z., Zhou, S., & Guan, J. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2285814 · Cited by 59
Perspective-SIFT: An efficient tool for low-altitude remote sensing image registration
Cai, G., Jodoin, P., Li, S., Wu, Y., Su, S., & Huang, Z. (2013) · Signal Processing · doi:10.1016/j.sigpro.2013.04.008 · Cited by 60
Automatic Remote-sensing Image Registration Using SURF
Bouchiha, R., & Besbes, K. (2013) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2013.v5.653 · Cited by 59
Perceptual Image Hashing With Texture and Invariant Vector Distance for Copy Detection
Huang, Z., & Liu, S. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2999188 · Cited by 58
Photo-based question answering
YEH, T. P., Lee, J. J., & Darrell, T. (2008) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1459359.1459412 · Cited by 59
A Model of Visual Attention for Natural Image Retrieval
Liu, G., & Fan, D.-P. (2013) · International Conference on Information Science and Cloud Computing Companion (ISCC-C) · doi:10.1109/iscc-c.2013.21 · Cited by 58
A real-time embedded architecture for SIFT
Zhong, S., Wang, J., Yan, L., Kang, L., & Cao, Z. (2012) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2012.09.002 · Cited by 58
Face Recognition Using Gabor Wavelet Features with PCA and KPCA - A Comparative Study
Vinay, A., Shekhar, V. S., Murthy, K. N. B., & Natarajan, S. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.07.434 · Cited by 57
Feature integration of EODH and Color-SIFT: Application to image retrieval based on codebook
Tian, X., Jiao, L., Liu, X., & Zhang, X. (2014) · Signal Processing Image Communication · doi:10.1016/j.image.2014.01.010 · Cited by 57
Recognizing places using spectrally clustered local matches
Olson, E. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2009.07.021 · Cited by 58
Classifying Compound Structures in Satellite Images: A Compressed Representation for Fast Queries
Gueguen, L. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2348864 · Cited by 57
Rotation and Illumination Invariant Interleaved Intensity Order-Based Local Descriptor
Dubey, S. R., Singh, S. K., & Singh, R. K. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2358879 · Cited by 56
Image search—from thousands to billions in 20 years
Zhang, L., & Rui, Y. (2013) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2490823 · Cited by 58
Albarelli, A., Rodolà, E., & Andrea Torsello, A. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0432-4 · Cited by 56
Johnson, M., Brostow, G., Shotton, J., Arandjelović, O., Kwatra, V., & Cipolla, R. (2006) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2006.00960.x · Cited by 56
Juang, C.-F., & Chen, G. (2012) · IEEE transactions on industrial electronics (1982. Print) · doi:10.1109/tie.2011.2159949 · Cited by 54
Binary Coherent Edge Descriptors
Zitnick, C. L. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15552-9_13 · Cited by 55
Survey of Landmark-based Indoor Positioning Technologies
Jang, B., Kim, H., & Kim, J. W. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.08.013 · Cited by 55
MATRIOSKA: A Multi-level Approach to Fast Tracking by Learning
Maresca, M. E., & Petrosino, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41184-7_43 · Cited by 56
SIFT-Rank: Ordinal description for invariant feature correspondence
Toews, M., & Wells, W. M. (2009) · 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.2009.5206849 · Cited by 55
Evaluation of binary keypoint descriptors
Bekele, D. A., Teutsch, M., & Schuchert, T. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738753 · Cited by 54
Fuzzy Density Peaks Clustering
Bian, Z., Chung, F.-L., & Wang, S. (2020) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.2985004 · Cited by 54
Dhaya, R. (2021) · Journal of Innovative Image Processing · doi:10.36548/jiip.2021.1.002 · Cited by 54
Wound area measurement with 3D transformation and smartphone images
Liu, C., Fan, X., Guo, Z., Mo, Z.-J., Chang, E., & Xu, Y. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3308-1 · Cited by 52
Scalability of local image descriptors
Lejsek, H., Ásmundsson, F. H., Jónsson, B. Þ., & Amsaleg, L. (2006) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1180639.1180760 · Cited by 54
Discriminative vision-based recovery and recognition of human motion
Poppe, R. W. (2009) · doi:10.3990/1.9789036528108 · Cited by 54
A Near-Term Quantum Computing Approach for Hard Computational Problems in Space ExplorationPreprint
Smelyanskiy, V., Rieffel, E. G., Knysh, S., Williams, C. P., Johnson, M. W., Thom, M. C., Macready, W. G., & Pudenz, K. L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1204.2821 · Cited by 74
Performance evaluation of feature detection and matching in stereo visual odometry
Jiang, Y., Yun-xi, X., & Liu, Y. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.06.055 · Cited by 52
IMORL: Incremental Multiple-Object Recognition and Localization
He, H., & Chen, S. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2001774 · Cited by 53
Face recognition based on the multi-scale local image structures
Geng, C., & Jiang, X. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.03.011 · Cited by 53
Fast and robust 3D feature extraction from sparse point clouds
Serafin, J., Olson, E., & Grisetti, G. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759604 · Cited by 53
Remote sensing image mosaic technology based on SURF algorithm in agriculture
Zhang, W., Li, X., Yu, J., Kumar, M., & Mao, Y. (2018) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-018-0323-5 · Cited by 53
Integrating SIFT and CNN Feature Matching for Partial-Duplicate Image Detection
Zhou, Z., Wu, Q. M. J., Wan, S., Sun, W., & Sun, X. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2019.2909936 · Cited by 53
Self-Similarity Features for Multimodal Remote Sensing Image Matching
Xiong, X., Jin, G., Xu, Q., & Zhang, H. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3131489 · Cited by 52
High-Resolution Face Verification Using Pore-Scale Facial Features
Li, D., Zhou, H., & Lam, K. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2412374 · Cited by 52
A novel image retrieval method based on multi-trend structure descriptor
Zhao, M. Y., Zhang, H., & Sun, J. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.02.016 · Cited by 52
3D reconstruction of disaster scenes for urban search and rescue
Verykokou, S., Ioannidis, C., Athanasiou, G., Doulamis, N. D., & Amditis, A. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5450-y · Cited by 52
A Comparative Study of Mobile-Based Landmark Recognition Techniques
Yap, K., Chen, T., Li, Z., & Wu, K. (2010) · IEEE Intelligent Systems · doi:10.1109/mis.2010.12 · Cited by 51
Mining Compact Bag-of-Patterns for Low Bit Rate Mobile Visual Search
Ji, R., Duan, L., Chen, J., Huang, T., & Gao, W. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2324291 · Cited by 51
Dense Segmentation-Aware Descriptors
Trulls, E., Kokkinos, I., Sanfeliu, A., & Moreno-Noguer, F. (2013) · 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.2013.372 · Cited by 51
An improvement to the SIFT descriptor for image representation and matching
Liao, K., Liu, G., & Hui, Y. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.03.021 · Cited by 52
Efficiently matching sets of features with random histograms
Dong, W., Wang, Z., Charikar, M., & Li, K. (2008) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1459359.1459384 · Cited by 51
Dense SIFT and Gabor descriptors-based face representation with applications to gender recognition
Wang, J.-G., Li, J., Lee, C. Y., & Yau, W. (2010) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2010.5707370 · Cited by 49
Identifying Join Candidates in the Cairo Genizah
Wolf, L., Littman, R., Mayer, N., German, T., Dershowitz, N., Shweka, R., & Choueka, Y. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0389-8 · Cited by 51
Evaluation of feature-based methods for automated network orientation
Apollonio, F. I., Ballabeni, A., Gaiani, M., & Fabio Remondino, A. (2014) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-5-47-2014 · Cited by 51
Deformation and illumination invariant feature point descriptor
Moreno-Noguer, F. (2011) · doi:10.1109/cvpr.2011.5995529 · Cited by 51
Pattern of Local Gravitational Force (PLGF): A Novel Local Image Descriptor
Bhattacharjee, D., & Roy, H. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2930192 · Cited by 51
Is There Anything New to Say About SIFT Matching?
Bellavia, F., & Colombo, C. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01297-z · Cited by 51
From frequent itemsets to semantically meaningful visual patterns
Yuan, J., Wu, Y. N., & Yang, M. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281284 · Cited by 52
Keyframe Retrieval by Keypoints: Can Point-to-Point Matching Help?
Zhao, W., Jiang, Y., & Ngo, C. (2006) · Lecture notes in computer science · doi:10.1007/11788034_8 · Cited by 52
Towards low bit rate mobile visual search with multiple-channel coding
Ji, R., Duan, L., Chen, J., Yao, H., Rui, Y., Chang, S., & Gao, W. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072372 · Cited by 50
Coding Visual Features Extracted From Video Sequences
Baroffio, L., Cesana, M., Redondi, A. E. C., Tagliasacchi, M., & Tubaro, S. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2312617 · Cited by 51
Research and improvement of feature detection algorithm based on FAST
Li, Y., Zheng, W., Liu, X., Mou, Y., Yin, L., & Yang, B. (2021) · RENDICONTI LINCEI · doi:10.1007/s12210-021-01020-1 · Cited by 49
FeatureBooster: Boosting Feature Descriptors with a Lightweight Neural Network
Wang, X., Liu, Z., Hu, Y., Xi, W., Yu, W., & Zou, D. (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/cvpr52729.2023.00737 · Cited by 49
SmartEye: Real-time and efficient cloud image sharing for disaster environments
Hua, Y., He, W., Liu, X., & Feng, D. (2015) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2015.7218541 · Cited by 49
Qian, J., Yang, J. J., & Gao, G. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.03.005 · Cited by 49
A Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery
Long, T., Jiao, W., He, G., & Zhang, Z. (2016) · Remote Sensing · doi:10.3390/rs8010056 · Cited by 49
Joint Multi-View Hashing for Large-Scale Near-Duplicate Video Retrieval
Nie, X., Jing, W., Cui, C., Zhang, C., Zhu, L., & Yin, Y. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2913383 · Cited by 49
Deep Spatial-Temporal Neural Network for Classification of EEG-Based Motor Imagery
Qiao, W., & Bi, X. (2019) · International Conference on Artificial Intelligence and Computer Science (AICS) · doi:10.1145/3349341.3349414 · Cited by 47
A fast and efficient image retrieval system based on color and texture features
Singh, C., & Kaur, K. P. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.10.002 · Cited by 48
A Two-Stage Triplet Network Training Framework for Image Retrieval
Min, W., Mei, S., Li, Z., & Jiang, S. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2974326 · Cited by 48
Image-based indoor positioning system
Kawaji, H., Hatada, K., Yamasaki, T., & Aizawa, K. (2010) · doi:10.1145/1878039.1878041 · Cited by 48
Roadmap to High Throughput Phenotyping for Plant Breeding
Kim, J. Y. (2020) · Journal of Biosystems Engineering · doi:10.1007/s42853-020-00043-0 · Cited by 46
An Efficient Near-Duplicate Video Shot Detection Method Using Shot-Based Interest Points
Zhou, X., Zhou, X., Chen, L., Bouguettaya, A., Xiao, N., & Taylor, J. (2009) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2009.2021794 · Cited by 48
Felzenszwalb, P. F. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130311 · Cited by 50
Local Polar DCT Features for Image Description
Song, T., & Li, H. (2012) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2229273 · Cited by 46
Unsupervised feature selection via distributed coding for multi-view object recognition
Christoudias, C. M., Urtasun, R., & Darrell, T. (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.2008.4587615 · Cited by 47
Lu, S., Liu, Z., & Shen, Y. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2790498 · Cited by 47
Configuration Estimates Improve Pedestrian Finding
Tran, D., & Forsyth, D. A. (2007) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 47
Multimodal Image Fusion Framework for End-to-End Remote Sensing Image Registration
Li, L., Han, L., Ding, M., & Cao, H. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3247642 · Cited by 51
Localizing Multiple Objects Using Radio Tomographic Imaging Technology
Wang, Q., Yi̇ği̇tler, H., Jäntti, R., & Huang, X. (2015) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2015.2432038 · Cited by 46
TDHPPIR: An Efficient Deep Hashing Based Privacy-Preserving Image Retrieval Method
Zhang, C., Zhu, L., Zhang, S., & Yu, W. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.119 · Cited by 46
Visual Model Feature Tracking For UAV Control
Mondragon, I. F., Campoy, P., Correa, J. F., & Mejías, L. (2007) · IEEE International Symposium on Intelligent Signal Processing, · doi:10.1109/wisp.2007.4447629 · Cited by 46
Combination of Multiple Global Descriptors for Image RetrievalPreprint
Jun, H., Ko, B., Kim, Y., Kim, I., & Kim, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.10663 · Cited by 59
Evolutionary learning of local descriptor operators for object recognition
Pérez, C. B., & Gustavo Olague, A. (2009) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1569901.1570043 · Cited by 45
Discriminative features for image classification and retrieval
Liu, S., & Bai, X. P. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.12.008 · Cited by 44
Relative ranking of facial attractiveness
Altwaijry, H., & Belongie, S. (2013) · Proceedings · doi:10.1109/wacv.2013.6475008 · Cited by 45
An Intensity-augmented Ordinal Measure for Visual Correspondence
Mittal, A., & Ramesh, V. (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.2006.56 · Cited by 45
Automated Municipal Solid Waste Sorting for Recycling Using a Mobile Manipulator
Gundupalli, S. P., Hait, S., & Thakur, A. (2016) · doi:10.1115/detc2016-59842 · Cited by 45
Context-aware obstacle detection for navigation by visually impaired
Gharani, P., & Karimi, H. A. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.06.002 · Cited by 45
On-Board GNSS/IMU Assisted Feature Extraction and Matching for Oblique UAV Images
Jiang, S., & Jiang, W. (2017) · Remote Sensing · doi:10.3390/rs9080813 · Cited by 46
Correlated Primary Visual Texton Histogram Features for Content Base Image Retrieval
Raza, A., Dawood, H., Dawood, H., Shabbir, S., Mehboob, R., & Banjar, A. (2018) · IEEE Access · doi:10.1109/access.2018.2866091 · Cited by 45
Ground Feature Oriented Path Planning for Unmanned Aerial Vehicle Mapping
Liu, C., Zhang, S., & Akbar, A. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2899369 · Cited by 45
Recent advances in local feature detector and descriptor: a literature survey
Joshi, K., & Patel, M. I. (2020) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-020-00200-3 · Cited by 47
Pseudo-Siamese Graph Matching Network for Textureless Objects’ 6-D Pose Estimation
Wu, C., Chen, L., He, Z., & Jiang, J. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3070501 · Cited by 45
Real-time 3D Camera Tracking for Industrial Augmented Reality Applications
Bleser, G., Pastarmov, Y., & Stricker, D. (2005) · Digital Library (University of West Bohemia) · Cited by 45
Drift-free tracking of rigid and articulated objects
Gall, J., Rosenhahn, B., & Seidel, H. (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.2008.4587558 · Cited by 45
Ground Truth Data, Content, Metrics, and Analysis
Krig, S. (2014) · Apress eBooks · doi:10.1007/978-1-4302-5930-5_7 · Cited by 48
Supervised Kernel Descriptors for Visual Recognition
Wang, P., Wang, J., Zeng, G., Xu, W., Zha, H., & Li, S. (2013) · 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.2013.368 · Cited by 43
Rank-SIFT: Learning to rank repeatable local interest points
Li, B., Xiao, R., Li, Z., Cai, R., Lu, B., & Zhang, L. (2011) · 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.2011.5995461 · Cited by 44
Compression of image patches for local feature extraction
Makar, M. A., Chang, C.-L., Chen, D. M., Tsai, S. S., & Girod, B. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4959710 · Cited by 44
Steel Surface Defects Detection and Classification Using SIFT and Voting Strategy
Suvdaa, B., Ahn, J., & Ko, J. (2013) · Cited by 44
Image Stitching System Based on ORB Feature-Based Technique and Compensation Blending
Adel, E., Elmogy, M. M., & El-Bakry, H. M. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.060907 · Cited by 44
Structure from motion using dense CNN features with keypoint relocalization
Widya, A. R., Torii, A., & Okutomi, M. (2018) · IPSJ Transactions on Computer Vision and Applications · doi:10.1186/s41074-018-0042-y · Cited by 44
Wang, J., Li, W., Zhang, M., & Chanussot, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3333401 · Cited by 43
Local Log-Euclidean Covariance Matrix (L2ECM) for Image Representation and Its Applications
Li, P., & Wang, Q. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33712-3_34 · Cited by 43
PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems
Belarbi, M. A., Mahmoudi, S., & Belalem, G. (2017) · International Journal of Ambient Computing and Intelligence · doi:10.4018/ijaci.2017100104 · Cited by 43
FRIST—flipping and rotation invariant sparsifying transform learning and applications
Wen, B., Ravishankar, S., & Bresler, Y. (2017) · Inverse Problems · doi:10.1088/1361-6420/aa6c6e · Cited by 43
Metric Localization with Scale-Invariant Visual Features Using a Single Perspective Camera
Bennewitz, M., Stachniss, C., Burgard, W., & Behnke, S. (2006) · Springer eBooks · doi:10.1007/11681120_16 · Cited by 43
Metadata
- Authors
- Ke, Y., & Sukthankar, R.
- Affiliations
- Carnegie Mellon University (US)
- Intel (United States) (US)
- Venue
- Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
- Publisher
- IEEE
- Published
- 2004-11-12
- Volume
- 2
- Pages
- 506-513
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2145072179
- Cited by
- 3,237
- References
- 19
- Citations by year
- 2026: 8 · 2025: 36 · 2024: 50 · 2023: 72 · 2022: 84 · 2021: 129 · 2020: 113 · 2019: 159 · 2018: 156 · 2017: 229 · 2016: 223 · 2015: 261 · 2014: 265 · 2013: 254 · 2012: 217
- FWCI
- 53.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- 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 › Artificial Intelligence › Computer Science and Engineering
- Keywords
- PCA-SIFT, local image descriptors, SIFT, principal component analysis, local feature detection, image registration, object recognition, image retrieval, feature matching
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
