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Title: Variants of Orthogonal Neighborhood Preserving Projections for Image Recognition
Researcher: Koringa, Purvi Amrutlal
Guide(s): Mitra, Suman K
Keywords: Engineering and Technology,Computer Science,Imaging Science and Photographic Technology
University: Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)
Completed Date: 2018
Abstract: With the increase in the resolution of image capturing sensors and data storage newlinecapacity, a huge increase in image data is seen in past decades. This information newlineupsurge has created a huge challenge for machines to perform tasks such as image newlinerecognition, image reconstruction etc. In image data, each observation or a pixel newlinecan be considered as a feature or a dimension, thus an image can be represented as a data point in the very high-dimensional space. Most of these high-dimensional newlineimages lie on or near a low-dimensional manifold. Performing machine learning newlinealgorithms on this high-dimensional data is computationally expensive and usually newlinegenerates undesired results because of the redundancy present in the image newlinedata. Dimensionality Reduction (DR) methods exploit this redundancy within newlinethe high-dimensional image space and explore the underlying low-dimensional manifold structure based on some criteria or image properties such as correlation, similarity, pair-wise distances or neighborhood structure. newline
Pagination: xii, 111p.
Appears in Departments:Department of Information and Communication Technology

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01_title.pdfAttached File82.43 kBAdobe PDFView/Open
02_declaration and certificate.pdf79.41 kBAdobe PDFView/Open
03_acknowledgements.pdf79.97 kBAdobe PDFView/Open
04_table of contents.pdf115.97 kBAdobe PDFView/Open
05_abstract.pdf58.2 kBAdobe PDFView/Open
06_list of tables and figures.pdf116.28 kBAdobe PDFView/Open
07_chapter 1.pdf192.35 kBAdobe PDFView/Open
08_chapter 2.pdf1.16 MBAdobe PDFView/Open
09_chapter 3.pdf141.34 kBAdobe PDFView/Open
10_chapter 4.pdf1.79 MBAdobe PDFView/Open
11_chapter 5.pdf1.11 MBAdobe PDFView/Open
12_chapter 6.pdf110.84 kBAdobe PDFView/Open
13_references.pdf111.41 kBAdobe PDFView/Open

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