Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Spatial Weighting for Bag-of-Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
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
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Efficient highly over-complete sparse coding using a mixture model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Image classification using super-vector coding of local image descriptors
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Discriminative affine sparse codes for image classification
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A probabilistic model for recursive factorized image features
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Discriminative spatial pyramid
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Beyond spatial pyramids: Receptive field learning for pooled image features
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Ask the locals: Multi-way local pooling for image recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Modeling spatial layout with fisher vectors for image categorization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Recently, spatial partitioning approaches such as spatial pyramid matching (SPM) are commonly used in image classification to collect the global and local features of the images. They divide the input image into small sub-regions (typically in a hierarchical manner) and generate a feature vector for each of them. Although the codes for the descriptors are assigned softly in modern image feature representation techniques, the codes must fall into only a single sub-region when forming the feature vector. In other words, the soft code assignment is used in the descriptor space but the codes are still "hard" voted from the view point of the image space. This paper proposes a spatial soft voting method, in which the existence of the codes are expressed by a Gaussian function and the maps of the existence are sampled to form a feature vector. The generated feature vectors are "soft" both in the descriptor space and the image space. In addition, extra computational cost as compared to SPM is negligibly small. The concept of the spatial soft voting is general and can be applied to most hard spatial partitioning approaches.