Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Kernel sparse representation for image classification and face recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Image classification using super-vector coding of local image descriptors
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
CENTRIST: A Visual Descriptor for Scene Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-dimensional signature compression for large-scale image classification
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Sparse kernel approximations for efficient classification and detection
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Discriminative spatial saliency for image classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Three things everyone should know to improve object retrieval
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Modeling spatial layout with fisher vectors for image categorization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Constructing global image representations from local feature descriptors is a common step in most visual classification tasks. Traditionally, the Bag of Features (BoF) representations involving hard vector quantization have been used ubiquitously for such tasks. Recent works have demonstrated superior performance of soft assignments over hard assignments. Fisher vector representations have been shown to outperform other global representations on most benchmark datasets. Fisher vectors (i) use soft assignments, and (ii) reduce information loss due to quantization by capturing the deviations from the mean. However, the Fisher vector representations are huge and the representation size increases linearly with the vocabulary size. Recent findings report that the classification performance of Fisher vectors is proportional to the vocabulary size. Computational and storage requirements, however, discourage the use of arbitrarily large vocabularies. Also, Fisher vectors are not inherently discriminative. In this paper, we devise a novel strategy to compute sparse Fisher representations. This allows us to increase the vocabulary size with little computation and storage overhead and still attain the performance of a larger vocabulary. Further, we describe an approach to encode class-discriminative information in the Fisher vectors. We evaluate our method on four popular datasets. Empirical results show that our representations consistently outperform the traditional Fisher Vector representations and are comparable to the state of art approaches.