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Feature Extraction for Image Classification and Analysis with Ant Colony Optimization Using Fuzzy Logic Approach

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Author :  Subba Rao Katteda, C Naga Raju and Maddala Lakshmi Bai

Affiliation :  St. Ann's College of Engineering and Technology

Country :  India

Category :  Digital Signal & Image Processing

Volume, Issue, Month, Year :  2, 4, December, 2011

Abstract :


The problem of structure extraction from the image which contains many clustered objects is a challenging one for high level image analysis. When an image contains many clustered objects overlapping of objects can cause for hiding the structure. The existing segmentation techniques for better understanding, not able to the address the constituent parts of the image implicitly. The approaches like multistage segmentation address to some extent, but for each stage a separate structure is extracted, and thus causes for the ambiguity about the structure. The proposed approach called Ant Colony Optimization and Fuzzy logic based technique resolves this problem, and gives the implicit structure, that meets with original structure. The segmentation approach uses the swarm intelligence technique based on the behavior of the ant colonies. The segmentation is the process of separating the non-overlapping regions that constitute an image. The segmentation is important for structured and non-structured image analysis and classification for better understanding.

Keyword :  Ant Colony Optimization (ACO), Fuzzy Logic, Segmentation, Classification, Analysis.

Journal/ Proceedings Name :  Signal & Image Processing: An International Journal (SIPIJ)

URL :  https://aircconline.com/sipij/V2N4/2411sipij12.pdf

User Name : Ryan cooper
Posted 27-04-2022 on 16:15:36 AEDT



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