Soft computing represents a collection of techniques, such as neural networks, evolutionary computation, fuzzy logic, and probabilistic reasoning. As - posed to conventional qhardq computing, these techniques tolerate impre- sion and uncertainty, similar to human beings. In the recent years, successful applications of these powerful methods have been published in many dis- plines in numerous journals, conferences, as well as the excellent books in this book series on Studies in Fuzziness and Soft Computing. This volume is dedicated to recent novel applications of soft computing in multimedia processing. The book is composed of 21 chapters written by experts in their respective fields, addressing various important and timely problems in multimedia computing such as content analysis, indexing and retrieval, recognition and compression, processing and filtering, etc. In the chapter authored by Guan, Muneesawang, Lay, Amin, and Lee, a radial basis function network with Laplacian mixture model is employed to perform image and video retrieval. D. Androutsos, P. Androutsos, Plataniotis, and Venetsanopoulos investigate color image indexing and retrieval within a small-world framework. Wu and Yap develop a framework of fuzzy relevance feedback to model the uncertainty of users' subjective perception in image retrieval.2.1 Multiple-Instance Learning Given a set of instances x1, x, ..., xy, the task in a typical machine learning problem is to ... such as k-NN algorithms , Support Vector Machine (SVM) , and EM combined with DD  are proposed to solve MIL.
|Title||:||Intelligent Multimedia Processing with Soft Computing|
|Author||:||Yap Peng Tan, Kim-Hui Yap, Lipo Wang|
|Publisher||:||Springer - 2006-09-15|