Learning-Based Local Visual Representation and Indexing
Learning-Based Local Visual Representation and Indexing by Rongrong Ji, published by Elsevier S & T in 2015, is a comprehensive examination of contemporary methods in visual content representation and indexing. This 128-page book is presented in English and focuses on innovative techniques in learning-based visual representation, alongside discussions on emerging topics in local representation and advancements in content-based visual search methods.
Readers will find a detailed review of state-of-the-art procedures in learning-based local visual representation, as well as guidance on mastering essential techniques for constructing large-scale visual search engines and indexing systems. The book also provides insights into the application of machine learning techniques to enhance visual recognition systems, particularly in the area of visual feature representation. This edition serves as a valuable resource for professionals and students in fields related to computer science, artificial intelligence, and computer vision.
Official synopsis Publisher
Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques.
- Discusses state-of-the-art procedures in learning-based local visual representation.
- Shows how to master the basic techniques needed for building a large-scale visual search engine and indexing system
- Provides insight into how machine learning techniques can be leveraged to refine the visual recognition system, especially in the part of visual feature representation.
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