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
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. . Wavelet transform can be used to characterize textures using statistical properties of the gray levels of the pixels comprising a surface image . 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 the existing wavelet histogram technique. The efficiency in terms Recall rate and retrieval speed is tested with five types of images and the results reflect the importance of wavelets in CBIR.Fnorm theory along with progressive retrieval strategy improves retrieval performance. The rest of this paper is organized as follows. In section 2, we introduced the general structure of the proposed CBIR system. Section 3 provides the image decomposition using wavelets. Section 4 describes Feature Extraction and Similarity criteria. Section 5 presents a progressive CBIR strategy. Section 6 describes the implementation and experimental results. Finally conclusions are offered in section 7.
F-norm theory , progressive image retrieval strategy.
Content-Based Image Retrieval (CBIR) is considered as the process of retrieving desired images from huge databases based on extracted features from the image themselves (without resorting to a key word). Features are derived directly from the images and they are extracted and analyzed by means of computer processing.CBIR is a bottleneck of the access of multimedia databases that deal with text, audio, video and image data which could provide us with enormous amount of information . Many commercial and research CBIR systems have been built and developed (e.g.: QBIC, Virage, Pichunter, visual SEEK, Chabot, Excalibur, photobook, Jacob) . Content based image retrieval -, allowing to automatically extract targets according to objective visual contents of image itself(e.g. color, texture and shape) has become increasingly attractive, in Multimedia Information Service System (MISS). With appealing time frequency localization and multi-scale properties, wavelet transform proved to be effective in visual feature extraction and representation. It can be used to characterize textures
2. General structure of Proposed CBIR system
Our proposed CBIR algorithm is based on decomposition of the database...
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