Colour-Texture Fusion In Image Segmentation For Content-Based Image Retrieval Systems
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Date
2007-02
Authors
WOI SENG, OOI
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Abstract
With the advances in computer technologies and the popularity of the World
Wide Web, the volume of digital images has grown rapidly. In parallel with this growth,
content-based image retrieval (CBIR) is becoming a fast growing research area in
recent years. Image segmentation is an important pre-processing step which has a
great influence on the performance of CBIR systems. In this research, a novel image
segmentation framework, dedicated to region queries in CBIR, is presented. The
underlying technique is based on the fusion of colour and texture features by a
modified fuzzy c-means clustering (FCM) algorithm. For each image pixel, the colour
components of the CIELAB colour space are combined with texture features, computed
from the Grey-Level Co-occurrence Matrix (GLCM), to form regions that exhibit
homogeneous properties. A region merging algorithm is applied to recursively merge
non-dominant regions. Then, the visual properties of each region are indexed and
used in a query. To evaluate the effectiveness and applicability of the proposed
method, a series of experiments using outdoor and satellite scene images has been
performed. The proposed method shows superior segmentation performances when
compared with those from existing CBIR prototype systems, i.e., Istorama and
Blobworld. Through the retrieval results and the precision-recall analysis, it is
demonstrated that the proposed system is effective, and compares favorably with
global and local histogram methods.
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Keywords
Colour-Texture Fusion In Image Segmentation , For Content-Based Image Retrieval Systems