Modeling the spatial layout of images beyond spatial pyramids
Pattern Recognition Letters
Sparse discriminative Fisher vectors in visual classification
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Pooling in image representation: The visual codeword point of view
Computer Vision and Image Understanding
Spatially local coding for object recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Spatial graph for image classification
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Beyond spatial pyramid matching: spatial soft voting for image classification
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Spatio-temporal layout of human actions for improved bag-of-words action detection
Pattern Recognition Letters
Multiple spatial pooling for visual object recognition
Neurocomputing
Image Classification with the Fisher Vector: Theory and Practice
International Journal of Computer Vision
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We introduce an extension of bag-of-words image representations to encode spatial layout. Using the Fisher kernel framework we derive a representation that encodes the spatial mean and the variance of image regions associated with visual words. We extend this representation by using a Gaussian mixture model to encode spatial layout, and show that this model is related to a soft-assign version of the spatial pyramid representation. We also combine our representation of spatial layout with the use of Fisher kernels to encode the appearance of local features. Through an extensive experimental evaluation, we show that our representation yields state-of-the-art image categorization results, while being more compact than spatial pyramid representations. In particular, using Fisher kernels to encode both appearance and spatial layout results in an image representation that is computationally efficient, compact, and yields excellent performance while using linear classifiers.