Uses
The Caltech 101 dataset has been used to train and test several Computer Vision recognition and classification algorithms. The first paper to make use of Caltech 101 was an incremental Bayesian approach to one shot learning. One shot learning is an attempt to learn a class of object using only a few examples, by building off of prior knowledge of many other classes.
The Caltech 101 images, along with the annotations, were used for another one shot learning paper at Caltech.
L. Fei-Fei, R. Fergus and P. Perona. One-Shot learning of object categories
Other Computer Vision papers that report using the Caltech 101 dataset:
- Shape Matching and Object Recognition using Low Distortion Correspondence. Alexander C. Berg, Tamara L. Berg, Jitendra Malik. CVPR 2005
- The Pyramid Match Kernel:Discriminative Classification with Sets of Image Features. K. Grauman and T. Darrell. International Conference on Computer Vision (ICCV), 2005
- Combining Generative Models and Fisher Kernels for Object Class Recognition Holub, AD. Welling, M. Perona, P. International Conference on Computer Vision (ICCV), 2005
- Object Recognition with Features Inspired by Visual Cortex. T. Serre, L. Wolf and T. Poggio. Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), IEEE Computer Society Press, San Diego, June 2005.
- SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition. Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik. CVPR, 2006
- Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. CVPR, 2006
- Empirical study of multi-scale filter banks for object categorization, M.J. Mar韓-Jim閚ez, and N. P閞ez de la Blanca. December 2005
- Multiclass Object Recognition with Sparse, Localized Features, Jim Mutch and David G. Lowe., pg. 11-18, CVPR 2006, IEEE Computer Society Press, New York, June 2006
- Using Dependent Regions or Object Categorization in a Generative Framework, G. Wang, Y. Zhang, and L. Fei-Fei. IEEE Comp. Vis. Patt. Recog. 2006
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