Multimedia Data Mining And Knowledge Discovery 1st Edition Valery A Petrushin Auth by Valery A. Petrushin (auth.), Valery A. Petrushin Ms, Phd, Latifur Khan Bs, Ms, Phd (eds.) 9781846284366, 9781846287992, 1846284368, 1846287995 instant download after payment.

Multimedia information is ubiquitous and essential in many applications, and repositories of multimedia are numerous and extremely large. Consequently, researchers and professionals need new techniques and tools for extracting the hidden, useful knowledge embedded within multimedia collections, thereby helping them discover relationships between the various elements and using this knowledge in decision-making applications.

Multimedia Data Mining and Knowledge Discovery, assembling the work of leading academic and professional/industrial researchers worldwide, provides an overview of the current state-of-the-art in the field of multimedia data mining and knowledge discovery, and discusses the variety of hot topics in multimedia data mining research. Consisting of an introductory section and four topical parts, the book describes the objectives and current tendencies in multimedia data mining research and their applications. Each part contains an overview of its chapters and leads the reader with a structured approach through the diverse subjects in the field.

Topics and Features:

• Features a comprehensive introduction to multimedia data mining and its relevance today

• Presents a global perspective of the field and its various components

• Provides broad, yet thorough and detailed coverage of the subject

• Numerous chapters reference websites with supplementary materials and demonstrations

• Explores multimedia data exploration, multimedia data modeling and evaluation, and visualization

• Offers an entire part devoted to applications and case studies

Written with graduate students in mind, this much needed comprehensive survey of the current state of multimedia data mining and knowledge discovery will also serve as a valuable resource for researchers with interests in multimedia data mining, summarization, indexing, and retrieval.