Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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.
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)