Similar shape retrieval in image database system

Pao Yue-kong Library Electronic Theses Database

Similar shape retrieval in image database system

 

Author: Tam, Kam-shing
Title: Similar shape retrieval in image database system
Degree: M.Sc.
Year: 2001
Subject: Image processing
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Electronic and Information Engineering
Pages: v, 51 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1599573
URI: http://theses.lib.polyu.edu.hk/handle/200/2453
Abstract: The ability to extract invariant features from an image is important in the field of pattern recognition and image processing. By using the invariant features, any object in the image can then be identified independently of its position, size and orientation. In this dissertation, different shape representation methods will be investigated and an appropriate one will be selected and evaluated. Furthermore, efficient methods, which combine the different shape features into a unified high-dimensional invariant feature set for discriminatory object retrieval, will be described. Experiments have been conducted based on a database consisting of 50 images taken from multicolored man-made or natural objects in real world scenes. A set of shape descriptors is firstly developed to measure the roughness of contours. A color image is then converted into a binary image. The respective contours in the image are then obtained and their corresponding shape descriptors are generated. These shape descriptors, including shape factor, invariant moments, and Fourier descriptors, are computed and evaluated for shape retrieval in this dissertation. Finally, their applicability for classification is studied by using the combination of these shape features. Furthermore, an image retrieval system will be constructed in order to access or retrieve images from a database based on a query image. At the moment, we will restrict ourselves to the case where an object is rigid and can be described by its boundary. Also, we will deal only with non-overlapping boundaries that are completely known (ideal input). We will assume, furthermore, that the classes can be described by a few fixed prototypes that may be rotated, translated and scaled. The objects can possess some random noise on their boundaries, such a situation may arise, for example, in the recognition of machine parts in automated assembly. The best classification result of 50 images from 5 classes using the combined shape features with the nearest-neighbor classification was 96%.

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