Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/2433
Title: Adaptive segmentaion and similarity measures in content-based image retrieval
Authors: Ricky Purnomo.
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
DRNTU::Engineering::Computer science and engineering::Information systems::Database management
Issue Date: 2000
Abstract: With the advances in digital imaging, the accompanying increase in the number of digital images, and subsequent creation of image databases in digital libraries, image retrieval has emerged as a problem which merits some attention. This thesis aims to investigate particularly natural (general) image retrieval. A short review of works on image retrieval is presented and followed by description of the colour and texture features used in this project, the L*a*b* colour space and the construction of Gabor filter bank. A new adaptive image segmentation method based on Fuzzy C-Means is developed for region extraction from an image. This algorithm is developed to support region based image retrieval. Several methods used in the segmentation algorithm for finding the most accurate number of regions are evaluated, with the one using fast merging Davies Bouldin index giving a reasonably good result, trading off accuracy for speed. Global and region based image retrieval methods using Nearest Neighbour distance, modified Nearest Feature Line distance, and Region Match Distance are investigated. While all of them yields relatively good results, some performance characteristics unique to certain combination of image types/classes and methods are analyzed.
URI: http://hdl.handle.net/10356/2433
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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