Generalized adaptive Bayesian Relevance Feedback for image retrieval in the Orthogonal Polynomials Transform domain

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.