Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/24400
Title: Investigations on a few image Classification methods and Techniques for improving Classification performance
Researcher: Sujatha, K S
Guide(s): Vinod, B
Keywords: Information and Communication engineering
Macro Precision
Mean Average Precision
Semantic categories
Upload Date: 2-Sep-2014
University: Anna University
Completed Date: 01-09-2013
Abstract: As the number of images to be stored in personal collections public newlinedata sets and the internet is becoming huge and ever growing it becomes newlinecrucial to develop computationally efficient methods for organizing and newlinesearching images Therefore the ability to classify images into semantic newlinecategories and objects is essential in order to manage and organize the newlinecollection of images on a database The problem is challenging because the newlineappearance of object instances varies substantially owing to changes in pose newlineimaging and lighting conditions, occlusions and within class shape variations newlineIdeally the representation should be flexible enough to cover a wide range of newlinevisually different classes, each with large within category variations while newlineretaining good discriminative power between the classes In this research newlinethe problem of improving the classification performance of various classes newlineof images like objects semantic scenes fine grained textile designs and newline fabrics are explored from a supervised learning perspective Hence this thesis newlineis a result of the study and comparative evaluation of different methodologies for automatic image classification in terms of its degree of recognition using performance measures like accuracy rate Macro Precision Micro Precision Mean Average Precision MAP and F1 measure newline newline newline
Pagination: xxv, 226p.
URI: http://hdl.handle.net/10603/24400
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificate.pdf1.03 MBAdobe PDFView/Open
03_abstract.pdf15.42 kBAdobe PDFView/Open
04_acknowledgement.pdf6.44 kBAdobe PDFView/Open
05_content.pdf50.01 kBAdobe PDFView/Open
06_chapter1.pdf245.27 kBAdobe PDFView/Open
07_chapter2.pdf48.07 kBAdobe PDFView/Open
08_chapter3.pdf267.13 kBAdobe PDFView/Open
09_chapter4.pdf517.79 kBAdobe PDFView/Open
10_chapter5.pdf183.92 kBAdobe PDFView/Open
11_chapter6.pdf204.32 kBAdobe PDFView/Open
12_chapter7.pdf671.4 kBAdobe PDFView/Open
13_chapter8.pdf1.53 MBAdobe PDFView/Open
14_chapter9.pdf35.91 kBAdobe PDFView/Open
15_reference.pdf38.35 kBAdobe PDFView/Open
16_publication.pdf5.84 kBAdobe PDFView/Open
17_vitate.pdf6.12 kBAdobe PDFView/Open


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