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Proceedings Paper

Improved image retrieval based on fuzzy colour feature vector
Author(s): Ahlam M. Ben-Ahmeida; Ahmed Y. Ben Sasi
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Paper Abstract

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

Paper Details

Date Published: 14 March 2013
PDF: 10 pages
Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87686C (14 March 2013); doi: 10.1117/12.2021159
Show Author Affiliations
Ahlam M. Ben-Ahmeida, College of Industrial Technology (Libya)
Ahmed Y. Ben Sasi, College of Industrial Technology (Libya)


Published in SPIE Proceedings Vol. 8768:
International Conference on Graphic and Image Processing (ICGIP 2012)
Zeng Zhu, Editor(s)

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