Effective Distance Measures for Color-Content Based Image Retrieval

Abstract

Content-based image retrieval systems are the challenging area of research in computer science. A retrieval method which uses color feature is implemented in this paper. Effectiveness of various distance measures such as euclidean distance, histogram intersection, histogram quadratic distance and canberra distance are also implemented to determine similarity between feature vectors. Results are compared to determine performance of each of the distance measure during image retrieval. The experimental results show that histogram quadratic distance measure produced more accurate results for image retrieval as compared to other distance measures.

liedE � ht��U�oH) convolutional codes and compared with the (2,1,7) convolutional codes. The proposed interleaver is applied to different bits stream lengths of 1024, 2048, 4096, 8192, and 16384 bits. The simulation results show the superiority of the proposed chaotic interleaved convolutional codes scheme over the traditional schemes in the image transmission over the mobile channels with respect the shorter constraint length of the encoder. Also, the chaotic interleaver performs better with the packet length increasing. The chaotic interleaver enhances the security with the different secret key for every transmitted packet. The computer simulations are carried out using the widely accepted Jakes’ model. The results reveal that the proposed scenarios can be applied to the long bit stream packets technologies such as Wimax.