Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/8005
Title: APPLICATIONS OF GENETIC ALGORITHM IN CONTENT BASED IMAGE RETRIEVAL
Authors: Murala, Subhanayam
Keywords: ELECTRICAL ENGINEERING
GENETIC ALGORITHM
CONTENT BASED IMAGE RETRIEVAL
IMAGE RETRIEVAL
Issue Date: 2009
Abstract: With the emergence and increased popularity of the World Wide Web Over the past decade, retrieval of image based on content, often referred to as CBIR, has gained a lot research interests. On the WWW where many images can be found, it is convenient to search for target images in possibly very large image databases by presenting query images as examples. Thus, more and more web search engines are now equipped with CBIR facilities for retrieving images on a query-by-image-examples basis. The image retrieval is based on the similarity measurement between query and image database. Images with high similarity to query are judge more relevant to the query and should be retrieved first. The Gabor Wavelet Correlogram method is based on a combination of multi-resolution image decomposition and color correlation histogram. According to the new algorithm, Gabor wavelet coefficients of the image are computed first using a directional Gabor wavelets transform. A quantization step is then applied before computing one-directional Auto-Correlogram of the Gabor wavelet coefficients. Finally, index vectors are constructed using this one-directional Gabor Wavelet Correlogram. The performance of the proposed method is compared with recent start of art techniques. The quantization step is more important for Gabor Wavelet Correlogram based image indexing and retrieval. A novel evolutionary method called Evolutionary Group Algorithm (EGA) is proposed for complicated time-consuming optimization problems such as finding optimal parameters of content-based image indexing algorithms. In the new evolutionary algorithm, the image database is partitioned into several smaller subsets, and each subset is used by an updating process as training patterns for each chromosome during evolution. This is in contrast to Genetic Algorithms that use the whole database as training patterns for evolution. The optimal quantization thresholds computed by EGA improved significantly all the evaluation measures including average precision and average recall for the Wavelet Correlogram method.
URI: http://hdl.handle.net/123456789/8005
Other Identifiers: M.Tech
Appears in Collections:MASTERS' DISSERTATIONS (Electrical Engg)

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