Feature Representation and Extraction for Image Search and Video Retrieval -- Learning and Recognition Methods for Image Search and Video Retrieval -- Improved Soft Assignment Coding for Image Classification -- Inheritable Color Space (InCS) and Generalized InCS Framework with Applications to Kinship Verification -- Novel Sparse Kernel Manifold Learner for Image Classification Applications -- A New Efficient SVM (eSVM) with Applications to Accurate and Efficient Eye Search in Images -- SIFT Features in Multiple Color Spaces for Improved Image Classification -- Clothing Analysis for Subject Identification and Retrieval -- Performance Evaluation of Video Analytics for Traffic Incident Detection and Vehicle Counts Collection.

This book initially reviews the major feature representation and extraction methods and effective learning and recognition approaches, which have broad applications in the context of intelligent image search and video retrieval. It subsequently presents novel methods, such as improved soft assignment coding, Inheritable Color Space (InCS) and the Generalized InCS framework, the sparse kernel manifold learner method, the efficient Support Vector Machine (eSVM), and the Scale-Invariant Feature Transform (SIFT) features in multiple color spaces. Lastly, the book presents clothing analysis for subject identification and retrieval, and performance evaluation methods of video analytics for traffic monitoring. Digital images and videos are proliferating at an amazing speed in the fields of science, engineering and technology, media and entertainment. With the huge accumulation of such data, keyword searches and manual annotation schemes may no longer be able to meet the practical demand for retrieving relevant content from images and videos, a challenge this book addresses.