A survey of content-based image retrieval with high-level semantics
Pattern Recognition
A content-based image retrieval scheme allowing for robust automatic personalization
Proceedings of the 6th ACM international conference on Image and video retrieval
An evaluation study of clustering algorithms in the scope of user communities assessment
Computers & Mathematics with Applications
IEEE Transactions on Image Processing
Enhanced human behavior recognition using HMM and evaluative rectification
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Bayesian filter based behavior recognition in workflows allowing for user feedback
Computer Vision and Image Understanding
Concept-based indexing of annotated images using semantic DNA
Engineering Applications of Artificial Intelligence
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In this paper, a novel relevance feedback algorithm is proposed for improving the performance of interactive content-based retrieval systems. The algorithm recursively estimates the similarity measure, which is used for data ranking in description environments where similarity-based queries are applied, using a set of relevant/irrelevant samples feedback by the user to the system so that the adjusted response is a better approximation of the current user's information needs and preferences. In particular, using concepts of functional analysis, the similarity measure is expressed as a parametric form of known monotone increasing functional components. Then, the contribution of each functional component to the similarity measure is estimated through a recursive and efficient on-line learning algorithm so that: 1) the current user's needs and preferences, as indicated by a set of selected relevant/irrelevant samples, are satisfied as much as possible, while simultaneously 2) a minimal modification of the already estimated similarity measure is accomplished. Experimental results on a large real-life database using objective evaluation criteria, such as the precision-recall curve and the average normalized modified retrieval rank (ANMRR), indicate that the proposed scheme outperforms the compared ones. In addition, the proposed algorithm requires low computational complexity and it can be implemented in a recursive way.