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Query by image content is a method to retrieve the most important images from the image database. It is an answer for the problem of searching for digital images in large database. A large number of relevance feedback schemes have been developed to improve the performance of content based image retrieval. In this paper we propose biased discriminant Euclidean embedding that form intraclass geometry...
Traditionally image is retrieved with the help of the associated tag which is added to the image while storing it in the database. This text based image retrieval is time consuming, laborious and expensive. In order to overcome these flaws content based image retrieval is proposed which avoid the use of textual description and retrieve the image based on their visual similarity. To achieve this images...
An approach to search images from a databases by structural features got from the three stages clustering algorithm is considered. Image key forming investigation for visual pattern processing is presented.
With the development of image retrieval system, the organizing and managing of image database effectively have been a key technology of users' retrieving. This paper firstly makes use of improved ant colony clustering algorithm in emotional clustering which based on image feature. This algorithm may identify who is the first ant by calculating the Euclidean distance, simulate ants' action of picking...
We propose a probabilistic model for the relevance feedback of users looking for target images. This model takes into account user errors and user uncertainty about distinguishing similarly relevant images. Based on this model, we have developed an algorithm, which selects images to be presented to the user for further relevance feedback until a satisfactory image is found. In each query session,...
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