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This paper presents a novel framework for Content Based Image Retrieval(CBIR), which combines color, texture and spatial structure of image. The proposed method uses color, texture and spatial structure descriptors to form a feature vector. Images are segmented into regions to extract local color, texture and CENTRIST(CENsus Transform hISTogram) features respectively. Multiple-instance learning (MIL)...
This paper proposed a novel method, which uses gray level co-occurrence matrix (GLCM) and singular value decomposition (SVD) to extract the features of texture images for image retrieval. In our algorithm, we take the top ten singular values which are based on SVD as the feature vectors, and then uses the Euclidean distance for similarity measure. Compared with traditional GLCM-based feature extraction...
Retrieving image from large & varied collections using image content (such as color, shape, texture) as a key is challenging & important problem . This paper describes a two approaches to content based image retrieval (CBIR) that represent each image in database by a vector of feature values called ??Image Retrieval using DCT coefficients of pixel distribution & average value of row &...
Research on image retrieval technology based on color feature, for the color histogram with a rotation, translation invariance of the advantages and disadvantages of lack of space, a color histogram and color moment combination image retrieval. The theory is a separate color images and color histogram moment of extraction, and then two methods of extracting color feature vector weighted to achieve...
Based on the analysis of color histogram for image retrieval, a new descriptor, bit-plane distribution feature (BPDF), is proposed in this paper. The image is firstly divided into eight bit-planes. Meantime, the Gray code of bit-planes is used to avoid the effect of changes in the intensity values on bit-planes. Then, according to the distribution of each bit-plane, a feature vector is constructed...
This paper focuses on finding the image features in both spatial and wavelet domain and applied that for greenery and non-greenery image classification. The spatial color feature called guided color coherence feature(GCCF) & guided distance feature in wavelet(GDFW) are calculated for the purpose of classification. The color coherence vector is constructed for all images and is used to find three...
In this paper, we propose a retrieval method using textual information retrieval techniques, such as vector space model, for images. Many image retrieval systems are proposed. However, these systems are mainly based on pattern recognition techniques. Therefore, the features of images are also based on these recognition techniques, such as color histogram, and shape of the object in images. Generally,...
With DICOM, information of patient can be stored with the actual images. Content-based access to medical images for supporting clinical decision-making has been proposed to ease the management of clinical data. The paper presents a method of medical image retrieval based on color-texture correlogram and GIT model for endoscopic images. First we define a new image feature called color-texture correlogram...
This paper proposes a region-based image retrieval approach using block discrete cosine transform (BDCT). In our retrieval system, for simplicity, an image is equally divided into four regions and an additional central region with one fourth size of the image. Therefore, an image is represented by five segmented regions, each of which is associated with a feature vector derived from BDCT. Users can...
A new technique to improve the performance of color based image retrieval is proposed. The proposed technique consists of two major phases. In the first phase, the SOM - self organizing map - is used to cluster images database based on some primitive features of the image. The result obtained through the first phase is used as a filtered set of images to be fed into the second phase which relies on...
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