A robust content-based image retrieval system using multiple features representations

The similarity measurements and the representation of the visual features are two important issues in content-based image retrieval (CBIR). In this paper, we compared between the combination of wavelet-based representations of the texture feature and the color feature with and without using the color layout feature. To represent the color information, we used global color histogram (GCH) beside the color layout feature and with respect to the texture information; we used Haar and Daubechies wavelets. Based on some commonly used Euclidean and Non-Euclidean similarity measures, we tested different categories of images and measured the retrieval accuracy when combining such techniques. The experiments showed that the combination of GCH and 2-D Haar wavelet transform using the cosine distance gives good results while the best results obtained when adding the color layout feature to this combination by using the Euclidean distance. The results reflected the importance of using the spatial information beside the color feature itself and the importance of choosing good similarity distance measurements.