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Learning mid-level features for recognition (2010)
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@MISC{Boureau10learningmid-level,
author = {Y-lan Boureau and Francis Bach and Yann LeCun and Jean Ponce},
title = {Learning mid-level features for recognition},
year = {2010}
}
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Abstract
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 performing coding scheme by learning a supervised discriminative dictionary for sparse coding. We provide theoretical and empirical insight into the remarkable performance of max pooling. By teasing apart components shared by modern mid-level feature extractors, our approach aims to facilitate the design of better recognition architectures.
Keyphrases
mid-level feature sparse coding mid-level feature extraction several recognition benchmark sift descriptor pooling step pointwise transformation remarkable performance intermediate complexity low-level descriptor recognition architecture many successful model relative importance gabor filter response coding step coded feature max pooling apart component modern mid-level feature extractor several type empirical insight object recognition soft vector quantization state-of-the-art performance several combination supervised discriminative dictionary comprehensive cross evaluation
