Colour-Texture Fusion In Image Segmentation For Content-Based Image Retrieval Systems
| dc.contributor.author | WOI SENG, OOI | |
| dc.date.accessioned | 2016-08-18T02:05:31Z | |
| dc.date.available | 2016-08-18T02:05:31Z | |
| dc.date.issued | 2007-02 | |
| dc.description.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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/2417 | |
| dc.subject | Colour-Texture Fusion In Image Segmentation | en_US |
| dc.subject | For Content-Based Image Retrieval Systems | en_US |
| dc.title | Colour-Texture Fusion In Image Segmentation For Content-Based Image Retrieval Systems | en_US |
| dc.type | Thesis | en_US |