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Napoleon, D.
- A Comparative Analysis of Clustering Algorithms for Content Based Image Retrieval
Abstract Views :320 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, School of Computer Science and Engineering, Bharathiar University, Coimbatore, IN
1 Department of Computer Science, School of Computer Science and Engineering, Bharathiar University, Coimbatore, IN
Source
Fuzzy Systems, Vol 4, No 3 (2012), Pagination: 112-114Abstract
Content based image retrieval is a set of techniques for retrieving semantically relevant images from an image data based on automatically derived image features. In CBIR, Image are indexed by their visual content, such as color, texture and shapes. Further research has suggested that the usage of clustering technique of image retrieval. For this paper we compare Fuzzy Possiblistic C-Means clustering algorithm for retrieving the most similar images. Inour experimental results shows that the modify Fuzzy Possiblistic Clustering Algorithm is better retrieval.Keywords
Query, Modify Fuzzy Possiblistic C-Means, Content-Based Image Retrieval.
- Classification of Image Using Fuzzy Lattice Neural Model
Abstract Views :166 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
3 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
1 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
3 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
Source
Fuzzy Systems, Vol 3, No 9 (2011), Pagination: 365-368Abstract
Computer hallucination, unlike humans, still has not fully acquired the facility to categories a person’s age group from an image of the person’s face. Successful gender and age classification could be used to boot the performance of face recognition system. Fuzzy models have been used and analyzed in this work to achieve the desired results. The concept of fuzzy lattice neural model is introduced and is applied to classify the age group of a person from the gray scale facial image. Next the fuzzy lattice relation model is constructed and is used to classify the age group of a person. Then the fuzzy lattice neural model is applied to segment an aerial gray scale image.