Learning Mid-Level Features For Recognition

Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of intermediate complexity. This process can often be broken down into two steps: (1) a coding step, which performs a pointwise transformation of the descriptors into a representation better adapted to the task, and (2) a pooling step, which summarizes the coded features over larger neighborhoods. Several combinations of coding and pooling schemes have been proposed in the literature. The goal of this paper is threefold. We seek to establish the relative importance of each step of mid-level feature extraction through a comprehensive cross evaluation of several types of coding modules (hard and soft vector quantization, sparse coding) and pooling schemes (by taking the average, or the maximum), which obtains state-of-the-art performance or better on several recognition benchmarks. We show how to improve the best perform...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Y-Lan Boureau, Francis Bach, Yann LeCun, Jean Ponce
• Keywords: Computer Vision, CVPR 2010, Pooling Schemes, Sparse Coding, Transform Low-level Descriptors