Learning Coarse-to-Fine Sparselets for Efficient Object Detection and Scene Classification

Abstract

Part model-based methods have been successfully applied to object detection and scene classification and have achieved state-of-the-art results. More recently the "sparselets" work [1-3] were introduced to serve as a universal set of shared basis learned from a large number of part detectors, resulting in notable speedup. Inspired by this framework, in this paper, we propose a novel scheme to train more effective sparselets with a coarse-to-fine framework. Specifically, we first train coarse sparselets to exploit the redundancy existing among part detectors by using an unsupervised single-hidden layer auto-encoder. Then, we simultaneously train fine sparselets and activation vectors using a supervised single-hidden-layer neural network, in which sparselets training and discriminative activation vectors learning are jointly embedded into a unified framework. In order to adequately explore the discriminative information hidden in the part detectors and to achieve sparsity, we propose to optimize a new discriminative objective function by imposing L0-norm sparsity constraint on the activation vectors. By using the proposed framework, promising results for multi-class object detection and scene classification are achieved on PASCAL VOC 2007, MIT Scene-67, and UC Merced Land Use datasets, compared with the existing sparselets baseline methods.