Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification

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Executive Summary

In recent years the Bag-of-Features (BoF) model has been extremely popular in image categorization. The method treats an image as a collection of unordered appearance descriptors extracted from local patches, quantizes them into discrete "Visual words", and then computes a compact histogram representation for semantic image classification, e.g. object recognition or scene categorization. The BoF approach discards the spatial order of local descriptors, which severely limits the descriptive power of the image representation. By overcoming this problem, one particular extension of the BoF model, called Spatial Pyramid Matching (SPM), has made a remarkable success on a range of image classification benchmarks like Caltech- 101and Caltech-256, and was the major component of the state-of-the-art systems

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