Caltech-256

Caltech-256 Object Category Dataset. We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 [1] was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category. Caltech-256 is collected in a similar manner with several improvements: a) the number of categories is more than doubled, b) the minimum number of images in any category is increased from 31 to 80, c) artifacts due to image rotation are avoided and d) a new and larger clutter category is introduced for testing background rejection. We suggest several testing paradigms to measure classification performance, then benchmark the dataset using two simple metrics as well as a state-of-the-art spatial pyramid matching [2] algorithm. Finally we use the clutter category to train an interest detector which rejects uninformative background regions.


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  1. Gao, Depeng; Liu, Jiafeng; Wu, Rui; Cheng, Dansong; Fan, Xiaopeng; Tang, Xianglong: Utilizing relevant RGB-d data to help recognize RGB images in the target domain (2019)
  2. Gou, Xu; Lu, Wei; Wang, Yi; Yan, Binyu; Xin, Mulin: Fisher-discriminative regularized latent sparse transfer model (2019)
  3. Flamary, Rémi; Cuturi, Marco; Courty, Nicolas; Rakotomamonjy, Alain: Wasserstein discriminant analysis (2018)
  4. Ghanta, Sindhu; Dy, Jennifer G.; Niu, Donglin; Jordan, Michael I.: Latent marked Poisson process with applications to object segmentation (2018)
  5. Schäfer, Dirk; Hüllermeier, Eyke: Dyad ranking using Plackett-Luce models based on joint feature representations (2018)
  6. Zheng, Charles; Achanta, Rakesh; Benjamini, Yuval: Extrapolating expected accuracies for large multi-class problems (2018)
  7. Cardoso, Douglas O.; Gama, João; França, Felipe M. G.: Weightless neural networks for open set recognition (2017)
  8. Mendes Júnior, Pedro R.; de Souza, Roberto M.; de O. Werneck, Rafael; Stein, Bernardo V.; Pazinato, Daniel V.; de Almeida, Waldir R.; Penatti, Otávio A. B.; da S. Torres, Ricardo; Rocha, Anderson: Nearest neighbors distance ratio open-set classifier (2017)
  9. Rawat, Waseem; Wang, Zenghui: Deep convolutional neural networks for image classification: a comprehensive review (2017)
  10. Ramírez-Corona, Mallinali; Sucar, L. Enrique; Morales, Eduardo F.: Hierarchical multilabel classification based on path evaluation (2016)
  11. Reineking, Thomas: Active classification using belief functions and information gain maximization (2016)
  12. Xie, Qunyi; Zhu, Hongqing: Image retrieval based on multiview constrained nonnegative matrix factorization and Gaussian mixture model spectral clustering method (2016)
  13. Blaes, Sebastian; Burwick, Thomas: Attentional bias through oscillatory coherence between excitatory activity and inhibitory minima (2015)
  14. Huang, Shuangping; Jin, Lianwen; Xue, Kunnan; Fang, Yuan: Online primal-dual learning for a data-dependent multi-kernel combination model with multiclass visual categorization applications (2015)
  15. Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang: Approximate kernel competitive learning (2015)
  16. Xie, Jianwen; Hu, Wenze; Zhu, Song-Chun; Wu, Ying Nian: Learning sparse FRAME models for natural image patterns (2015)
  17. Cong, Yang; Liu, Ji; Yuan, Junsong; Luo, Jiebo: Low-rank online metric learning (2014)
  18. Gong, Boqing; Grauman, Kristen; Sha, Fei: Learning kernels for unsupervised domain adaptation with applications to visual object recognition (2014)
  19. Kobayashi, Takumi: Kernel-based transition probability toward similarity measure for semi-supervised learning (2014)
  20. Lehmann, Alain D.; Gehler, Peter V.; van Gool, Luc: Branch&rank for efficient object detection (2014) ioport

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