Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Saliency, Scale and Image Description
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discriminant Analysis with Tensor Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Feature Hierarchies for Object Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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We propose a new approach to exploit the different discriminability of image features at different scales simultaneously. By modifying the Bag-of-words model, we represent an image as a matrix whose elements are the occurrences of a set of codewords within different scale ranges. In this way, we can represent an image collection using a 3rd-order tensor. Then a new classification method, tensor-pLSA, which is an extension of Probabilistic Latent Semantic Analysis (pLSA), is introduced to classify these images based on this tensor representation. Finally, we compare the tensor representation with the original matrix representation to show the effectiveness of our approach.