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In this paper, vocabulary tree based large-scale image retrieval scheme is proposed that can achieve higher accuracy and speed. The novelty of this paper can be summarized as follows. First, because traditional Scale Invariant Feature Transform (SIFT) descriptors are excessively concentrated in some areas of images, the extraction process of SIFT features is optimized to reduce the number. Then, combined...
Representation of image content is an important part of image annotation and retrieval, and it has become a hot issue in computer vision. As an efficient and accurate image content representation model, bag-of-words (BoW) has attracted more attention in recent years. After segmentation, BoW treats all of the image regions equally. In fact, some regions of image are more important than others in image...
Content-Based Image Retrieval (CBIR) characterizes the image content by extracting visual features, and measures the similarity according to the distance between the two features. This paper adopts CBIR to perform automatic tongue color analysis of Traditional Chinese Medicine (TCM). Firstly, we extract the visual features of tongue images to be analyzed, especially the color features; and then retrieve...
How to efficiently retrieve the images while preserving the user's privacy has gradually become a key problem in some applications such as Cloud storage, social networks. In this paper, a secure index used for image retrieval is constructed to protect the retrieval results being leaked to the malicious attackers. At first, inverted index is generated using visual words of images and then encrypted...
For the network environment with the limited transmission capacity, a multi-nodes image retrieval method based on visual words is proposed. Firstly, the visual words of query image are built by using the K-means clustering method after the color features and SIFT features of query image are extracted. Then the visual-words histogram of the query image is carried by the mobile Agent. The image similarity...
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...
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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