AI summary

This paper introduces Spatial Pyramid Matching for scene categorization, an extension of bag-of-features that uses multi-resolution histograms of local features to capture approximate geometric correspondence, achieving state-of-the-art results on the Caltech-101 database and high accuracy on a large database of fifteen natural scene categories.

Abstract

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba's "gist" and Lowe's SIFT descriptors.