The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study
Open accessOverview
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
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,703
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Self-citationVideo Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. J. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 4,568
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379
Texture features for browsing and retrieval of image data
Manjunath, B. S., & Ma, W. Y. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.531803 · Cited by 3,727
A Bayesian Hierarchical Model for Learning Natural Scene Categories
Li, F.-F., & Perona, P. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.16 · Cited by 3,613
Scale & Affine Invariant Interest Point Detectors
Mikolajczyk, K., & Schmid, C. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000027790.02288.f2 · Cited by 3,436
Self-citationA comparison of event models for naive bayes text classification
McCallum, A., & Nigam, K. (1998) · Cited by 3,289
Cited by
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
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524
Automated Flower Classification over a Large Number of Classes
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
Improving the Fisher Kernel for Large-Scale Image Classification
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
A Completed Modeling of Local Binary Pattern Operator for Texture Classification
Guo, Z., Zhang, L., & Zhang, D. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2044957 · Cited by 2,115
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0594-8 · Cited by 1,669
Self-citationImage Classification with the Fisher Vector: Theory and Practice
Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0636-x · Cited by 1,498
Is object localization for free? - Weakly-supervised learning with convolutional neural networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298668 · Cited by 886
Sampling Strategies for Bag-of-Features Image Classification
Nowak, E., Jurie, F., & Triggs, B. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744085_38 · Cited by 927
The devil is in the details: an evaluation of recent feature encoding methods
Chatfield, K., Lempitsky, V., Vedaldi, A., & Zisserman, A. (2011) · Procedings of the British Machine Vision Conference 2011 · doi:10.5244/c.25.76 · Cited by 834
Robust Part-Based Hand Gesture Recognition Using Kinect Sensor
Ren, Z., Yuan, J., Meng, J., & Zhang, Z. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2246148 · Cited by 746
Face Recognition Systems: A Survey
Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020) · Sensors · doi:10.3390/s20020342 · Cited by 623
PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571
“Unlike global features, local features are extracted from image patches centered at interesting points in an image [57,58].”
BackgroundFast Discriminative Visual Codebooks using Randomized Clustering Forests
Moosmann, F., Triggs, B., & Jurie, F. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0128 · Cited by 467
Discriminative Learning of Local Image Descriptors
Brown, M., Hua, G., & Winder, S. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.54 · Cited by 466
High-dimensional data clustering
Bouveyron, C., Girard, S., & Schmid, C. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.02.009 · Cited by 382
Self-citationLocal binary features for texture classification: Taxonomy and experimental study
Liu, L., Fieguth, P., Guo, Y., Wang, X., & Pietikäinen, M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.032 · Cited by 369
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
Lin, K., Lu, J., Chen, C.-S., & Zhou, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.133 · Cited by 363
50 Years of object recognition: Directions forward
Andreopoulos, A., & Tsotsos, J. K. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.04.005 · Cited by 347
Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost
Mensink, T., Verbeek, J., Perronnin, F., & Csurka, G. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33709-3_35 · Cited by 281
High-dimensional signature compression for large-scale image classification
Sanchez, J., & Perronnin, F. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995504 · Cited by 272
Noise or Signal: The Role of Image Backgrounds in Object Recognition
Xiao, K., Engstrom, L., Ilyas, A., & Madry, A. (2020) · arXiv · doi:10.48550/arxiv.2006.09994 · Cited by 161
Texture Description Through Histograms of Equivalent Patterns
Fernández, A., Álvarez, M. X., & Bianconi, F. (2013) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-012-0349-8 · Cited by 128
Ukwaththa, J., Herath, S., & Meddage, D. P. P. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.110294 · Cited by 118
Content based video matching using spatiotemporal volumes
Basharat, A., Zhai, Y., & Shah, M. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.016 · Cited by 94
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Song, A. H., Chen, R. J., Ding, T., Williamson, D. F. K., Jaume, G., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01099 · Cited by 79
Background selection schema on deep learning-based classification of dermatological disease
Zhou, J., Wu, Z., Jiang, Z., Huang, K., Guo, K., & Zhao, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105966 · Cited by 53
On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition
Shirahama, K., & Grzegorzek, M. (2017) · Electronics · doi:10.3390/electronics6020044 · Cited by 29
Shirahama, K., & Grzegorzek, M. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2292-8 · Cited by 24
Recognising complex activities with histograms of relative tracklets
Stein, S., & McKenna, S. J. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.08.012 · Cited by 11
Histopathology Slide Indexing and Search — Are We There Yet?
Shang, H. H., Nasr, M. S., Veerla, J. P., Saurav, J. R., Hajighasemi, A., Malidarreh, P., Huber, M., Moleta, C., Makker, J., & Luber, J. M. (2024) · NEJM AI · doi:10.1056/aics2300019 · Cited by 5
Automatic placental maturity grading via hybrid learning
Lei, B., Tan, E.-L., Chen, S., Li, W., Ni, D., Yao, Y., & Wang, T. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.10.033 · Cited by 5
Metadata
- Authors
- Zhang, J., Marszałek, M., Lazebnik, S., & Schmid, C.
- Venue
- International Journal of Computer Vision
- Published
- 2006-09-25
- Volume
- 73
- Issue
- 2
- Pages
- 213-238
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2112020727
- Cited by
- 1,923
- References
- 62
- FWCI
- 105.86
- Type
- journal article
- Language
- en
- Open access
- green
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.