ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Learning compact visual attributes for large-scale image classification
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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
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Spatial Pyramid Representation (SPR) is a widely used method for embedding both global and local spatial information into a feature, and it shows good performance in terms of generic image recognition. In SPR, the image is divided into a sequence of increasingly finer grids on each pyramid level. Features are extracted from all of the grid cells and are concatenated to form one huge feature vector. As a result, expensive computational costs are required for both learning and testing. Moreover, because the strategy for partitioning the image at each pyramid level is designed by hand, there is weak theoretical evidence of the appropriate partitioning strategy for good categorization. In this paper, we propose discriminative SPR, which is a new representation that forms the image feature as a weighted sum of semi-local features over all pyramid levels. The weights are automatically selected to maximize a discriminative power. The resulting feature is compact and preserves high discriminative power, even in low dimension. Furthermore, the discriminative SPR can suggest the distinctive cells and the pyramid levels simultaneously by observing the optimal weights generated from the fine grid cells.