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With information technology developing rapidly, variety and quantity of image data is increasing fast. How to retrieve desired images among massive images storage is getting to be an urgent problem. In this paper, we established a Distributed Image Retrieval System (DIRS), in which images are retrieved in a content-based way, and the retrieval among massive image data storage is speeded up by utilizing...
In this paper, a content-based audio retrieval method is proposed, which can quickly detect and locate similar sound in audio database. We extract a chroma-based audio feature: chromagram, a variation on time-frequency distributions, which represents the spectral energy at each of 12 pitch classes. Compared with traditional feature MFCC (Mel Frequency Cesptral Coefficient), chromagram is better when...
This paper presents a method for extracting texture and color hybrid features and constructing an adaptive weight operator, which can be used for content-based image retrieval (CBIR). This method extracts texture feature effectively based on Brushlet transform, quantifies in the HSV space, and extracts color feature by color histogram. K-mean clustering is introduced to count overall characteristics...
Content-Based Image Retrieval (CBIR) system is emerging as an important research area, users can search and retrieve images based on their properties such as shape, color and texture from the image database. Usually texture-based image retrieval just consider an original image of coarseness, contrast and roughness, actually there is much texture information in the edge image. This paper proposed a...
Region of interest(ROI) plays an important role in image analysis. In this paper, an efficient approach for content based image retrieval combining both color and texture features using three ROIs is proposed. Firstly, segment image to three parts using K-means algorithm. Secondly, select three ROIs from the three parts and then extract color features and texture features of ROIs. The similarity of...
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