Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/209153
Title: Generation of Compact and Effective Training set for Image Database Classification
Researcher: Jagruti K. Save
Guide(s): Kekre B. H
Keywords: Augmented Wang Database
COIL-100 Database
Evaluation of Classifier Model
Feature Extraction
Image Database
Image Transforms
PCA based Classification
Row/Column Mean Vector Generation
University: Narsee Monjee Institute of Management Studies
Completed Date: 09/08/2017
Abstract: In today s digital world, huge amount of images and videos are easily generated, accessed and shared. Before exploring and analyzing these images, it would be better to organize them in meaningful categories. So there is a need to make an automatic classifier which will classify these images according to their visual contents. newlineClassification is an important preprocessing step for content-based image retrieval (CBIR) system, especially when thousands of images are involved. There are two major steps in supervised classification system. Initially feature vectors for all training images are generated. This will be considered as the training set. Second step is to build the classifier using the training set. Accuracy of the classification system depends on many factors. The quality and the size of training set are the important factors. This work mainly focuses on the Generation of training set for classification. The original contribution to knowledge is the generation of an efficient and compact set of training feature vectors from given set of training images. Training and testing set of images are two disjoint sets. Features are extracted from images in transform domain. Since attention is on the first step of supervised classification system, the classifier is build using simple nearest neighbor (NN) classification. newline
Pagination: 
URI: http://hdl.handle.net/10603/209153
Appears in Departments:Department of Computer Engineering

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01_title page.pdfAttached File30.58 kBAdobe PDFView/Open
02_declaration.pdf25.28 kBAdobe PDFView/Open
03_certificate.pdf10.17 kBAdobe PDFView/Open
04_examinor certificate.pdf8.33 kBAdobe PDFView/Open
05_dedication.pdf36.06 kBAdobe PDFView/Open
06_acknowledgement.pdf40.23 kBAdobe PDFView/Open
07_abstract.pdf222.01 kBAdobe PDFView/Open
08_organization of the thesis.pdf214.94 kBAdobe PDFView/Open
09_contents.pdf238.34 kBAdobe PDFView/Open
10_list of figures.pdf229.33 kBAdobe PDFView/Open
11_list of tables.pdf178.57 kBAdobe PDFView/Open
12_list of abbreviation.pdf203.55 kBAdobe PDFView/Open
13_chapter 1.pdf1.11 MBAdobe PDFView/Open
14_chapter 2.pdf703.39 kBAdobe PDFView/Open
15_chapter 3.pdf989.68 kBAdobe PDFView/Open
16_chapter 4.pdf1.37 MBAdobe PDFView/Open
17_chapter 5.pdf2.27 MBAdobe PDFView/Open
18_chapter 6.pdf1.63 MBAdobe PDFView/Open
19_chapter 7.pdf490.47 kBAdobe PDFView/Open
20_chapter 8.pdf366.45 kBAdobe PDFView/Open
21_references.pdf468.81 kBAdobe PDFView/Open


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