CSPS: An Adaptive Pooling Method for Image Classification

This paper proposes an adaptive approach to learn class-specific pooling shapes (CSPS) for image classification. Prevalent methods for spatial pooling are often conducted on predefined grids of images, which is an ad-hoc method and, thus, lacks generalization power across different categories. In contrast, our CSPS is designed in a data-driven fashion by generating plenty of candidates and selecting the optimal subset for each class. Specifically, we establish an overcomplete spatial shape set that preserves as many geometric patterns as possible. Then, the class-specific subset is selected by training a linear classifier with structured sparsity constraints and color distribution cues. To address the high computational cost and the risk of overfitting due to the overcomplete scheme, the image representations for CSPS are first compressed according to dictionary sensitivity and shape importance. These representations are finally fed to SVMs for the classification task. We demonstrate that CSPS can learn compact yet discriminative geometric information for different classes that carries more semantic meaning than other methods. Experimental results on four datasets demonstrate the benefits of the proposed method compared with other pooling schemes and illustrate its effectiveness on both object and scene images.