Content Based Image Retrieval Using Segmentation Similarity Measure<br/>

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Content Based Image Retrieval Using Segmentation Similarity Measure

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 47555 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Jamzad, Mansour
Abstract:
Content Based Image Retrieval (CBIR) is a research area in computer vision. This area comprises of two main steps, low level feature extraction such as color, texture and shape extraction and also similarity measures for comparison of images. The challenge in this system is the existence semantic gap between the low level visual features and the high level image semantics. The aim of research in this field is to reduce this semantic gap. In this study the images are divided into regions using Meanshift method, for color segmentation and then moments of each region as color feature are calculated. Also for extracting texture the images are divided into regions using Jseg method, and then gabor filter is used to extract texture features of each regions. Then the similarty of images is calculated based on Mallows distance. The results shown in Chapter 5 suggests that the system based on the extraction of color and texture performs well individually. But the combination of color and texture does not show a promising performance
Keywords:
Color ; Textures ; Feature Vector ; Content Based Retrieval ; Image Retrieval ; Mallows Distance ; Semantic Gap

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