TY - CONF T1 - A novel feature descriptor based on the shearlet transform T2 - Image Processing (ICIP), 2011 18th IEEE International Conference on Y1 - 2011 A1 - Schwartz, W.R. A1 - da Silva,R.D. A1 - Davis, Larry S. A1 - Pedrini,H. KW - analysis;multiscale KW - analysis;object KW - classification;face KW - classification;image KW - classification;intensity KW - coefficients;image KW - descriptor;feature KW - detection;object KW - distribution KW - edge KW - EXTRACTION KW - extraction;image KW - gradient KW - gradients;histograms KW - identification;feature KW - methods;histograms KW - of KW - orientations;face KW - oriented KW - recognition;feature KW - recognition;shearlet KW - recognition;transforms; KW - shearlet KW - singularities;texture KW - texture;object KW - transform;signal AB - Problems such as image classification, object detection and recognition rely on low-level feature descriptors to represent visual information. Several feature extraction methods have been proposed, including the Histograms of Oriented Gradients (HOG), which captures edge information by analyzing the distribution of intensity gradients and their directions. In addition to directions, the analysis of edge at different scales provides valuable information. Shearlet transforms provide a general framework for analyzing and representing data with anisotropic information at multiple scales. As a consequence, signal singularities, such as edges, can be precisely detected and located in images. Based on the idea of employing histograms to estimate the distribution of edge orientations and on the accurate multi-scale analysis provided by shearlet transforms, we propose a feature descriptor called Histograms of Shearlet Coefficients (HSC). Experimental results comparing HOG with HSC show that HSC provides significantly better results for the problems of texture classification and face identification. JA - Image Processing (ICIP), 2011 18th IEEE International Conference on M3 - 10.1109/ICIP.2011.6115600 ER -