Creating efficient codebooks for visual recognition

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Creating efficient codebooks for visual recognition (2005)

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by Frederic Jurie , Bill Triggs

Venue:	In Proceedings of the IEEE International Conference on Computer Vision
Citations:	275 - 22 self

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@INPROCEEDINGS{Jurie05creatingefficient,
    author = {Frederic Jurie and Bill Triggs},
    title = {Creating efficient codebooks for visual recognition},
    booktitle = {In Proceedings of the IEEE International Conference on Computer Vision},
    year = {2005}
}

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Abstract

Visual codebook based quantization of robust appearance descriptors extracted from local image patches is an effective means of capturing image statistics for texture analysis and scene classification. Codebooks are usually constructed by using a method such as k-means to cluster the descriptor vectors of patches sampled either densely (‘textons’) or sparsely (‘bags of features ’ based on keypoints or salience measures) from a set of training images. This works well for texture analysis in homogeneous images, but the images that arise in natural object recognition tasks have far less uniform statistics. We show that for dense sampling, k-means over-adapts to this, clustering centres almost exclusively around the densest few regions in descriptor space and thus failing to code other informative regions. This gives suboptimal codes that are no better than using randomly selected centres. We describe a scalable acceptance-radius based clusterer that generates better codebooks and study its performance on several image classification tasks. We also show that dense representations outperform equivalent keypoint based ones on these tasks and that SVM or Mutual Information based feature selection starting from a dense codebook further improves the performance. 1.

Keyphrases

visual recognition efficient codebooks texture analysis natural object recognition task visual codebook equivalent keypoint several image classification task dense codebook training image local image patch image statistic robust appearance descriptor effective mean suboptimal code salience measure dense sampling scene classification k-means over-adapts descriptor space informative region mutual information dense representation scalable acceptance-radius descriptor vector feature selection uniform statistic homogeneous image

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