menu
{ "item_title" : "Data Management for Multimedia Retrieval", "item_author" : [" K. Selçuk Candan", "Maria Luisa Sapino "], "item_description" : "Multimedia data require specialized management techniques because the representations of color, time, semantic concepts, and other underlying information can be drastically different from one another. The user's subjective judgment can also have significant impact on what data or features are relevant in a given context. These factors affect both the performance of the retrieval algorithms and their effectiveness. This textbook on multimedia data management techniques offers a unified perspective on retrieval efficiency and effectiveness. It provides a comprehensive treatment, from basic to advanced concepts, that will be useful to readers of different levels, from advanced undergraduate and graduate students to researchers and to professionals. After introducing models for multimedia data (images, video, audio, text, and web) and for their features, such as color, texture, shape, and time, the book presents data structures and algorithms that help store, index, cluster, classify, and access common data representations. The authors also introduce techniques, such as relevance feedback and collaborative filtering, for bridging the semantic gap and present the applications of these to emerging topics, including web and social networking.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/0/52/188/739/0521887399_b.jpg", "price_data" : { "retail_price" : "81.00", "online_price" : "81.00", "our_price" : "81.00", "club_price" : "81.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Data Management for Multimedia Retrieval|K. Selçuk Candan

Data Management for Multimedia Retrieval

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

Multimedia data require specialized management techniques because the representations of color, time, semantic concepts, and other underlying information can be drastically different from one another. The user's subjective judgment can also have significant impact on what data or features are relevant in a given context. These factors affect both the performance of the retrieval algorithms and their effectiveness. This textbook on multimedia data management techniques offers a unified perspective on retrieval efficiency and effectiveness. It provides a comprehensive treatment, from basic to advanced concepts, that will be useful to readers of different levels, from advanced undergraduate and graduate students to researchers and to professionals. After introducing models for multimedia data (images, video, audio, text, and web) and for their features, such as color, texture, shape, and time, the book presents data structures and algorithms that help store, index, cluster, classify, and access common data representations. The authors also introduce techniques, such as relevance feedback and collaborative filtering, for bridging the "semantic gap" and present the applications of these to emerging topics, including web and social networking.

This item is Non-Returnable

Details

  • ISBN-13: 9780521887397
  • ISBN-10: 0521887399
  • Publisher: Cambridge University Press
  • Publish Date: May 2010
  • Dimensions: 10 x 7 x 1.1 inches
  • Shipping Weight: 2.3 pounds
  • Page Count: 500

Related Categories

You May Also Like...

    1

BAM Customer Reviews