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Advancement in hardware and telecommunication technology has boosted up creation and distribution of digital visual content. However this rapid growth of visual content creations has not been matched by the simultaneous emergence of technologies to support efficient image analysis and retrieval. Although there are attempt to solve this problem by using meta-data text annotation but this approach are not practical when it come to the large number of data collection. This system used 7 different feature vectors that are focusing on 3 main low level feature groups (color, shape and texture). This system will use the image that the user feed and search the similar images in the database that had similar feature by considering the threshold value. One of the most important aspects in CBIR is to determine the correct threshold value. Setting the correct threshold value is important in CBIR because setting it too low will result in less image being retrieve that might exclude relevant data. Setting to high threshold value might result in irrelevant data to be retrieved and increase the search time for image retrieval. Result show that this project able to increase the image accuracy to average 70% by combining 7 different feature vector at correct threshold value. iii

Item Type: Final Year Project
Academic Subject : Academic Department - Information Communication Technology
Subject: T Technology > T Technology (General)
Divisions: Sciences and Information Technology
Depositing User: Users 2053 not found.
Date Deposited: 24 Oct 2013 14:57
Last Modified: 25 Jan 2017 09:45
URI: http://utpedia.utp.edu.my/id/eprint/9783

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