{"product_id":"9783319074153","title":"SpringerBriefs in Computer Science","description":"\u003ch1\u003eSpringerBriefs in Computer Science\u003c\/h1\u003e \u003ch2\u003eHe, Ran; Hu, Baogang; Yuan, Xiaotong; Wang, Liang\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy.\u003c\/p\u003e\u003cp\u003eThe authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency, the brief introduces the additive and multiplicative forms of half-quadratic optimization to efficiently minimize entropy problems and a two-stage sparse presentation framework for large scale recognition problems. It also describes the strengths and deficiencies of different robust measures in solving robust recognition problems.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2014-09-09\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9783319074153\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-319-07416-0\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 110\u003c\/p\u003e ","brand":"Springer International Publishing","offers":[{"title":"Default Title","offer_id":44806977880204,"sku":"9783319074153","price":49.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783319074153.jpg?v=1772897605","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783319074153","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}