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Title: A Study On Time Optimization for Retrieving Images Using Colour
Researcher: S.NIRANJANAN
Guide(s): Dr.S.P.Rajagopalan
Upload Date: 24-May-2014
University: Vels University
Completed Date: 01-04-2014
Abstract: Image mining is the non-trivial extraction of implicit and actionable knowledge from large Image sets. It automates the detection and retrieval of relevant patterns in an Image base. Content Based Image Retrieval (CBIR) is an active research field in the past decades. Against the traditional system where the images are retrieved based on the key word search, CBIR systems retrieve the images based on the visual content. Even though some of the modern systems like relevance feedback system which improves the performance of CBIR systems exist, the importance of retrieving the images based on the low level features like Colour, Texture and Shape still determine the development of CBIR systems and cannot be undermined. Colour Histograms, Histogram Distance Measurements, Colour Spaces and Quantization play an important role in retrieving images based on similarities. newlineA Color quantization is a process that reduces the number of distinct colors used in an image, usually with the intention that the new image should be as visually similar as possible to the original image. The direct extraction of color feature from true color will lead to a large computation. In order to reduce the computation without a significant reduction in image quality, some representative color is extracted to represent the image thereby reducing the storage space and enhancing the process speed. The effect of color quantization on the performance of image retrieval has been reported by many authors. newlineThis research aims to develop a Quantization scheme which optimizes the time required to retrieve images which are similar in content to the query image using only colour with different histogram distance formulae. A Matlab program using these different quantization schemes is programmed to retrieve images from a fixed size database using a query image. The time consumed for displaying the resultant images is stored in a database along with the quantization variants data. The screen shots of retrieved images are also stored with appropriate names. newlineThe
Appears in Departments:School of Computing Sciences

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01_title.pdfAttached File44.29 kBAdobe PDFView/Open
02_certificate.pdf23.68 kBAdobe PDFView/Open
03_abstract.pdf19.46 kBAdobe PDFView/Open
04_declaration.pdf22.83 kBAdobe PDFView/Open
05_acknowledgement.pdf29.47 kBAdobe PDFView/Open
06_contents.pdf34.32 kBAdobe PDFView/Open
07_list of tables.pdf27.62 kBAdobe PDFView/Open
08_list of figures.pdf25.26 kBAdobe PDFView/Open
09_abbreviations.pdf28.92 kBAdobe PDFView/Open
10_chapter-1.pdf59.89 kBAdobe PDFView/Open
11_chapter-2.pdf57.38 kBAdobe PDFView/Open
12_chapert-3.pdf403.6 kBAdobe PDFView/Open
13_chapter-4.pdf435.88 kBAdobe PDFView/Open
14_chapter-5.pdf201.84 kBAdobe PDFView/Open
15_chapter-6.pdf52.56 kBAdobe PDFView/Open
16_appendix-a.pdf657.2 kBAdobe PDFView/Open
17_appendix-b.pdf2.7 MBAdobe PDFView/Open
18_appendix-c.pdf15.2 MBAdobe PDFView/Open
19_bibliography.pdf58.38 kBAdobe PDFView/Open

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