Results shown here are all trained using 30 samples from each category.
- Visualizing and Understanding Convolutional Networks (ARXIV 2013)
Cited 14 times. 70.6% ± 0.2% - Multipath Sparse Coding Using Hierarchical Matching Pursuit (CVPR 2013)
Cited 7 times. 50.7%
Additional info: Multipath Hierarchical Matching Pursuit
Link to paper's project page - Learning Subcategory Relevances for Category Recognition (CVPR 2008)
Cited 48 times. 49.5% - Spatially Local Coding for Object Recognition (ACCV 2010)
Cited 1 time. 46.6% ± 0.2%
Additional info: Multi-scale SIFT features extracted every 4 pixels.
Link to paper's project page
Link to paper's source code - On Feature Combination for Multiclass Object Detection (ICCV 2009)
Cited 376 times. 45.8%
Additional info: LP-β
Link to paper's project page (Contains results, source code and pre-computed features) - Image Classification using Random Forests and Ferns (2007)
Cited 412 times. 45.3% - Local Pyramidal Descriptors for Image Recognition (PAMI 2013)
Cited 1 time. 44.86%
Additional info: P-SIFT + Fisher encoding + SPM + Linear SVM
Link to paper's project page (Contains source code and demo) - A Binary Classification Framework for Two-Stage Multiple Kernel Learning (2012)
Cited 5 times. 44.8% - Efficient Learning of Sparse, Distributed, Convolutional Feature Representations for Object Recognition (ICCV 2011)
Cited 21 times. 42.05%
Additional info: CRBM K=4096 - In Defense of Nearest-Neighbor Based Image Classification (CVPR 2008)
Cited 478 times. 42%
Additional info: NBNN (5 descriptors) - Locality-constrained Linear Coding for Image Classification (CVPR 2010)
Cited 547 times. 41.19% - Local Naive Bayes Nearest Neighbor for Image Classification (2011)
Cited 20 times. 40.1% - Sparse Spatial Coding: A Novel Approach for Efficient and Accurate Object Recognition (ICRA 2012)
Cited 9 times. 37.08% ± 0.36% - Caltech-256 object categoriy dataset (2007)
Cited 596 times. 34.1% - Linear spatial pyramid matching using sparse coding for image classification (CVPR 2009)
Cited 713 times. 34.02% - Kernel codebooks for scene categorization (ECCV 2008)
Cited 242 times. 27.17%
3 comments:
This blog simply rocks! Tnx.
This blog simply rocks! Thank you for the efforts. Are there any changes in this table? Is it possible to see the execution speed?
@jmakov: Yes, I will continue to update this page whenever there is any updates. Regarding execution speed, I think it's a good idea. However, since not all author reveal their implementation's execution speed, it's not possible to know. I suggest you to contact the author directly if you need the information.
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