- Title
- Emergence phenomenon and fuzzy logic in meaningful image segmentation and retrieval
- Creator
- Deb, Sagarmay; Kulkarni, Siddhivinayak
- Date
- 2012
- Type
- Text; Book chapter
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/58429
- Identifier
- vital:5859
- Identifier
-
https://doi.org/10.4018/978-1-4666-1833-6.ch010
- Identifier
- ISBN:978-146661833-6
- Abstract
- Content-based image retrieval is a difficult area of research in multimedia systems. The research has proven extremely difficult because of the inherent problems in proper automated analysis and feature extraction of the image to facilitate proper classification of various objects. An image may contain more than one object, and to segment the image in line with object features to extract meaningful objects and then classify it in high-level like table, chair, car and so on has become a challenge to the researchers in the field. The latter part of the problem, the gap between low-level features like colour, shape, texture, spatial relationships, and high-level definitions of the images is called the semantic gap. Until this problem is solved in an effective way, the efficient processing and retrieval of information from images will be difficult to achieve. In this chapter, the authors explore the possibilities of how emergence phenomena and fuzzy logic can help solve these problems of image segmentation and semantic gap.
- Publisher
- Hershey IGI Global
- Relation
- Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 167-178
- Rights
- © Elsevier B.V., All rights reserved.
- Rights
- This metadata is freely available under a CCO license
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