Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Negative pseudo-relevance feedback in content-based video retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Adaptive relevance feedback based on Bayesian inference for image retrieval
Signal Processing - Special section on content-based image and video retrieval
Relevance feedback using adaptive clustering for image similarity retrieval
Journal of Systems and Software
A new integer image coding technique based on orthogonal polynomials
Image and Vision Computing
Indexing and retrieval of visually similar images in the orthogonal polynomials transform domain
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
IEEE Transactions on Image Processing
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In this paper, we propose a generalized Bayesian Relevance Feedback (RF) algorithm for image retrieval systems with enhanced adaptability to the users' requirements. The adaptability of the algorithm is owing to the different weights that are given to the current and the prior learning. This algorithm is implemented in an image retrieval system which learns in the integer-arithmetic Orthogonal Polynomials Transform (OPT) domain. With the transform's partial coefficients of the image being the features extracted, a mixture of Gaussians is used to represent the image. The image retrieval system is trained on the COIL-100 database. Experimental evidence illustrates the clear benefits of this introduction of adaptability into RF algorithm which can account for both positive and negative feedback. The superiority of the proposed algorithm in terms of increased recall and reduced number of feedback iterations when compared to the already existing Bayesian RF implementations is demonstrated.