2013/10/27 by Andrew Gleibman, Gleibman, Andrew · 1 citation
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Image Retrieval and Classification Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1310.7170
18 pages, 11 figures, 1 table
openalex publication_date 2013/10/27 · arxiv created 2013/12/09 · arxiv updated 2013/12/10 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
The first contribution of this paper is architecture of a multipurpose system, which delegates a range of object detection tasks to a classifier, applied in special grid positions of the tested image. The second contribution is Gray Level-Radius Co-occurrence Matrix, which describes local image texture and topology and, unlike common second order statistics methods, is robust to image resolution. The third contribution is a parametrically controlled automatic synthesis of unlimited number of numerical features for classification. The fourth contribution is a method of optimizing parameters C and gamma in LibSVM-based classifier, which is 20-100 times faster than the commonly applied method. The work is essentially experimental, with demonstration of various methods for definition of objects of interest in images and video sequences.