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Extracting More Relevant Features from Color Image for Enhancing the Image Retrieval Using Genetic Algorithm
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bajpai, Aruna |
| Copyright Year | 2016 |
| Abstract | soft computing and machine learning techniques are help to find the optimum patterns from raw data and utilize with different application to solve the real world problem. Thus the manner and the aspect of learning are changed as the application and their orientation is changed. In this presented paper we discuss a data mining technique which is used to find the relevant data more specifically the image data. The proposed model of image retrieval is based on the contents of the image therefore a review of the CBIR is prepared first and using the observed concepts a new method is tried to develop for enhancing the CBIR for obtaining the more relevant image from huge databases. The implementation and obtained performance of the proposed system demonstrate the efficiency and effective precision and recall rate during the classification analysis of the data. Additionally that improves the search time of the images using the query by image context. Keywords— CBIR, genetic algorithm, feature extraction, image data analysis, performance evaluation |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijcsit.com/docs/Volume%207/vol7issue2/ijcsit2016070269.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
