Content-based Image Retrieval Using Haar Wavelet Transform and Color Moment
Summary
Content-Based Image Retrieval (CBIR) allows to automatically extracting target images according to objective visual contents of the image itself. Representation of visual features and similarity match are important issues in CBIR. In this paper a novel CBIR method is proposed by exploit the wavelets which represent the visual feature. We use Haar and D4 wavelet to decompose color images into multilevel scale and wavelet coefficients, with which we perform image feature extraction and similarity match by means of F-norm theory. Furthermore, we also provide a progressive image retrieval strategy to achieve flexible CBIR. We tested five categories of color images in the experiments. The retrieval performance of D4 and Haar wavelet is compared with wavelet histograms in terms of recall rate and retrieval speed. Experiment results reflect the importance of wavelets in CBIR and F-norm theory along with progressive retrieval strategy achieves efficient retrieval.
Dr.B.C.Jinaga† Rector ,JNTU,India
V.S.K.Reddy††, IIT,Kharagpur.India
using statistical properties of the gray levels of the points/pixels comprising a surface image. In CBIR, wavelet approaches mainly include wavelet histogram and wavelet moment of image, etc. [12]. Wavelet transform can be used to characterize textures using statistical properties of the gray levels of the pixels comprising a surface image [13]. The wavelet transform is a tool that cuts up data or functions or operators into different frequency components and then studies each component with a resolution matched to its scale. In this paper, we used D4 and Haar wavelet transforms to decompose color images into multilevel scale and wavelet coefficients, with which we perform image feature extraction and similarity match by means of F-norm theory. We also present a progressive retrieval strategy, which contributes to flexible compromise between the retrieval speed and the recall rate. The retrieval performances are compared with
Content-Based Image Retrieval (CBIR) allows to automatically extracting target images according to objective visual contents of the image itself. Representation of visual features and similarity match are important issues in CBIR. In this paper a novel CBIR method is proposed by exploit the wavelets which represent the visual feature. We use Haar and D4 wavelet to decompose color images into multilevel scale and wavelet coefficients, with which we perform image feature extraction and similarity match by means of F-norm theory. Furthermore, we also provide a progressive image retrieval strategy to achieve flexible CBIR. We tested five categories of color images in the experiments. The retrieval performance of D4 and Haar wavelet is compared with wavelet histograms in terms of recall rate and retrieval speed. Experiment results reflect the importance of wavelets in CBIR and F-norm theory along with progressive retrieval strategy achieves efficient retrieval.
Dr.B.C.Jinaga† Rector ,JNTU,India
V.S.K.Reddy††, IIT,Kharagpur.India
using statistical properties of the gray levels of the points/pixels comprising a surface image. In CBIR, wavelet approaches mainly include wavelet histogram and wavelet moment of image, etc. [12]. Wavelet transform can be used to characterize textures using statistical properties of the gray levels of the pixels comprising a surface image [13]. The wavelet transform is a tool that cuts up data or functions or operators into different frequency components and then studies each component with a resolution matched to its scale. In this paper, we used D4 and Haar wavelet transforms to decompose color images into multilevel scale and wavelet coefficients, with which we perform image feature extraction and similarity match by means of F-norm theory. We also present a progressive retrieval strategy, which contributes to flexible compromise between the retrieval speed and the recall rate. The retrieval performances are compared with
References: [1] [2] [3] [4] Christopher C. Yang, “Content-based image retrieval: a comparison between query by example and image browsing map approaches “, Journal of Information Science, pp. 254-267, 2004. Rui Y. & Huang T. S., Chang S. F. “Image retrieval: current techniques, promising directions, and open issues”. Journal of Visual Communication and Image Representation, 10, 39-62, 1999. Vishal Chitkara, “Color-Based image Retrieval Using Binary Signatures“. Technical Report TR 01-08, University of Ulberta, Canada, May 2001. Qasim Iqbal and J. K. Aggarwal, “Combining Structure, Color, and Texture for Image Retrieval: A performance Evaluation”.16th International Conference on Pattern Recognition (ICPR), Quebec City, Canada, August 11-15. 2002, vol. 2, pp. 438-443 R. Calderbank, I. Daubechies, W. Sweldens and Y.Boon-Lock, “Wavelet transform that map integers to integers”,Appl. Comut. Harmon. Anal., 5(3), 332-369, 1998 [13] M.G. Mostafa, M.F. Tolba, T.F. Gharib, M.A. Megeed, Medical ImageSegmentation Using Wavelet based Multiresolution EM Algorithm”. IEEE International Conference on Industrial Electronics, Technology, &Automation., Cairo IETA’2001. Huihui Huang; Wei Huang; Zhigang Liu; Weirong Chen; Qingquan Qian; “Content-based color image retrieval via lifting scheme” Autonomous Decentralized Systems, 2005. ISADS Proceedings 2005. 4-8 April 2005 Page(s): 378 – 383. Henning Muller, Wolfgang Muller, David McG. Squire and Thierry Pun, “Performance Evaluation in Content-Based Image Retrieval: [5] [6] [7] [8]