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Despite the efforts to reduce the so-called semantic gap between the user's perception of image similarity and the feature-based representation of images, the interaction with the user remains fundamental to improve performances of content-based image retrieval (CBIR) systems. It is fact that the effectiveness of a CBIR system strongly depends on the set of visual features and the ‘metric’ used to...
Implicit feedback techniques take advantage of user behavior to understand user interests. The primary advantage to use implicit techniques is that such techniques remove the cost to the user of providing feedback. So, it is the cornerstone of both the search engine and information retrieval. In order to better understand user behaviors, this paper investigates three most important issues using implicit...
This paper presents a novel relevance feedback algorithm for image retrieval in content-based image retrieval systems based on the Logistic regression model. In order to narrow down the semantic gap between user's high-level query concepts and the low-level image features, user preferences are added to the algorithm. Based on modeling of user preferences as a probability distribution, the algorithm...
This paper presents a new relevance feedback (RF) method for image retrieval in content-based image retrieval (CBIR). The main conception of the method gives two aspects: First logistic regression adjusts the weight of each element in features extracted from the images in database with the preferences of the user. Then following a Bayesian methodology, which yields the posteriori of the images in...
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