Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/9091
Title: Content based image retrieval
Researcher: Thepade, Sudeep
Guide(s): Kekre, H B
Keywords: Content base
Image Retrieval
Upload Date: 23-May-2013
University: Narsee Monjee Institute of Management Studies
Completed Date: 2011
Abstract: Modern image search engines retrieve the images based on their visual contents, commonly referred to as Content Based Image newlineRetrieval (CBIR) systems. Typical CBIR systems can organize and newlineretrieve images from image databases, automatically by extracting newlinesome features such as color, texture, shape from images and newlinelooking for similar images which have similar feature. One problem newlineof this approach is reliance on visual similarity to judge semantic newlinesimilarity, which creates problems due to semantic gap between newlinelow-level content and high level concepts. Even with the subsistence newlineof this problem, if aggressive attempts are made CBIR can be used newlinefor real life applications. For example in spite of the open problems newlinelike robust text understanding, Google and Yahoo have become newlinemost popular for searching. newlineThe work presented here mainly focuses on efficient CBIR methods newlinewith help of representation of converting the visual content of newlineimages in feature vector using proposed techniques. The proposed newlineCBIR methods using Colour, Transformed Image, Texture and newlineShape content are proved to be better and faster using test bed of newline1000 variable size images spread across 11 image categories. newlineIn consideration of colour content as feature, the proposed newlineapproaches of using image colour averages and block truncation newlinecoding for CBIR are proposed. Image averaging techniques are newlinebased on taking averages of colour content of image, which can be newlineconsidered as feature vector for image retrieval. The image pixel newlinedata can be represented in form of the feature vectors with reduced newlinedimensions as row mean (RM), column mean (CM), forward newlinediagonal mean (FDM), backward diagonal mean (BDM). Use of the newlinecolour averaging techniques helps in obtaining faster and better newlineimage retrieval techniques. The FDM has been observed to give best newlineperformance among these colour averaging based CBIR methods.Image tiling is dividing image into equal and non-overlapping newlinesquare parts.
Pagination: 311p.
URI: http://hdl.handle.net/10603/9091
Appears in Departments:Department of Computer Engineering

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01_title.pdfAttached File115.15 kBAdobe PDFView/Open
02_table of content.pdf162.17 kBAdobe PDFView/Open
03_abstract.pdf105.72 kBAdobe PDFView/Open
04_chapter 1.pdf146.05 kBAdobe PDFView/Open
05_chapter 2.pdf162.29 kBAdobe PDFView/Open
06_chapter 3.pdf984.8 kBAdobe PDFView/Open
07_chapter 4 a.pdf706.42 kBAdobe PDFView/Open
08_chapter 4 b.pdf1.24 MBAdobe PDFView/Open
09_chapter 5.pdf842.82 kBAdobe PDFView/Open
10_chapter 6.pdf480.54 kBAdobe PDFView/Open
11_chapter 7.pdf409.8 kBAdobe PDFView/Open
12_references.pdf229.84 kBAdobe PDFView/Open
13_appendix i - ii.pdf6.09 MBAdobe PDFView/Open


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