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Recently, keypoint descriptors such as Scale Invariant Feature Transform (SIFT) have been proved promising in similarity retrieval of images, which adopts matching score as similarity. However, the matching score is easy to be decreased once there are little variances between image details, and hence lead to low retrieval performance. In this paper, we propose a novel retrieval approach that improves...
In this paper, authors propose a new learning based method for medical image retrieval which is based on fusing different features by linearly combining different similarities. Considering the abundant classes of medical images, this paper avoid to train a classifier for each class by using large amount training data. Instead, by using optimization method to combine different features' similarity,...
Aimed at the application requirements of content-based image retrieval technology on the Internet, firstly, some key techniques and application ways are researched, then the principles and methods how to reduce the ??gap?? between low-level visual features and high-level semantic description of image are analyzed for improving the efficiency and precision of image retrieval. At last, taken several...
This paper describes an effective framework to perform image segmentation and find regions of interest (ROI) in a user input object in an interactive way. Similar image objects are then retrieved from a repository. The repository stores off-line trained feature data of image objects, which was obtained by applying feature extraction and dimension reduction analysis to the ROI. The advantage of our...
Automatic Image Annotation (AIA) tries to minimize the manual effort for image annotation. However, the performance of the AIA approaches is not satisfactory. The interaction of user is needed to solve this problem. The annotation is refined during the interaction by using semantic-based relevance feedback. This approach has a limit as only the annotations of found images during the interaction are...
This paper proposes an efficient approach to find clusters of spatially related scene images collected from the website. Our method firstly builds a guide table, in which the ranked results are given according to the relevance scores of image pairs obtained by the image retrieval methods. Then the image clusters are generated by repeatedly choosing a seed image and performing query expansion directed...
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