Quadtree classified vector quantization based image retrieval scheme

With the fast development of multimedia, it is crucial to find the way to search image database effectively. The vector quantization (VQ) based image retrieval method is popular in recent years. In this paper, we propose the quadtree classified vector quantization (QCVQ) scheme to improve the VQ method by exploiting the visual importance of image blocks and using the edge information to describe the content of each block efficiently. Moreover, we also apply the adaptive block size. The simulation results show that, compared with the previous image retrieval algorithms using VQ and chromaticity moments (CM), our proposed scheme has obviously better average retrieval rate and higher average precision.

[1]  Gerald Schaefer.  Compressed domain image retrieval by comparing vector quantization codebooks , 2002, IS&T/SPIE Electronic Imaging.

[2]  Robert M. Gray,et al.  Image retrieval using color histograms generated by Gauss mixture vector quantization , 2004, Comput. Vis. Image Underst..

[3]  E. Salari,et al.  Efficient classification and codebook design for CVQ , 1996 .

[4]  Bhaskar Ramamurthi,et al.  Classified Vector Quantization of Images , 1986, IEEE Trans. Commun..

[5]  Guojun Lu,et al.  Image indexing and retrieval based on vector quantization , 2007, Pattern Recognit..

[6]  H. Dujmic,et al.  Local thresholding classified vector quantization with memory reduction , 2000, IWISPA 2000. Proceedings of the First International Workshop on Image and Signal Processing and Analysis. in conjunction with 22nd International Conference on Information Technology Interfaces. (IEEE.

[7]  Georgios S. Paschos,et al.  Image Content-Based Retrieval Using Chromaticity Moments , 2003, IEEE Trans. Knowl. Data Eng..

[8]  Guojun Lu,et al.  A novel image retrieval technique based on vector quantization , 1999 .

[9]  Chia-Hung Yeh,et al.  Content-based image retrieval through compressed indices based on vector quantized images , 2006 .