Conference Paper

Ask the Image: Supervised Pooling to Preserve Feature Locality

DOI: 10.1109/CVPR.2014.114 Conference: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


In this paper we propose a weighted supervised pooling method for visual recognition systems. We combine a standard Spatial Pyramid Representation which is commonly adopted to encode spatial information, with an appropriate Feature Space Representation favoring semantic information in an appropriate feature space. For the latter, we propose a weighted pooling strategy exploiting data supervision to weigh each local descriptor coherently with its likelihood to belong to a given object class. The two representations
are then combined adaptively with Multiple Kernel Learning. Experiments on common benchmarks (Caltech-
256 and PASCAL VOC-2007) show that our image representation improves the current visual recognition pipeline and it is competitive with similar state-of-art pooling methods. We also evaluate our method on a real Human-Robot Interaction setting, where the pure Spatial Pyramid Representation does not provide sufficient discriminative power, obtaining a remarkable improvement.

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Available from: Sean Ryan Fanello, May 27, 2014
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    ABSTRACT: In this paper, we propose a novel feature-space local pooling method for the commonly adopted architecture of image classification. While existing methods partition the feature space based on visual appearance to obtain pooling bins, learning more accurate space partitioning that takes semantics into account boosts performance even for a smaller number of bins. To this end, we propose partitioning the feature space over clusters of visual prototypes common to semantically similar images (i.e., images belonging to the same category). The clusters are obtained by Bregman co-clustering applied offline on a subset of training data. Therefore, being aware of the semantic context of the input image, our features have higher discriminative power than do those pooled from appearance-based partitioning. Testing on four datasets (Caltech-101, Caltech-256, 15 Scenes, and 17 Flowers) belonging to three different classification tasks showed that the proposed method outperforms methods in previous works on local pooling in the feature space for less feature dimensionality. Moreover, when implemented within a spatial pyramid, our method achieves comparable results on three of the datasets used.
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