A novel feature descriptor based on the shearlet transform

TitleA novel feature descriptor based on the shearlet transform
Publication TypeConference Papers
Year of Publication2011
AuthorsSchwartz WR, da Silva RD, Davis LS, Pedrini H
Conference NameImage Processing (ICIP), 2011 18th IEEE International Conference on
Date Published2011/09//
Keywordsanalysis;multiscale, analysis;object, classification;face, classification;image, classification;intensity, coefficients;image, descriptor;feature, detection;object, distribution, edge, EXTRACTION, extraction;image, gradient, gradients;histograms, identification;feature, methods;histograms, of, orientations;face, oriented, recognition;feature, recognition;shearlet, recognition;transforms;, shearlet, singularities;texture, texture;object, transform;signal

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.