A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function

Relevance feedback (RF) is an effective approach to bridge the gap between low-level visual features and high-level semantic meanings in content-based image retrieval (CBIR). The support vector machine (SVM) based RF mechanisms have been used in different fields of image retrieval, but they often treat all positive and negative feedback samples equally, which will inevitably degrade the effectiveness of SVM-based RF approaches for CBIR. In fact, positive and negative feedback samples, different positive feedback samples, and different negative feedback samples all always have distinct properties. Moreover, each feedback interaction process is usually tedious and time-consuming because of complex visual features, so if too many times of iteration of feedback are asked, users may be impatient to interact with the CBIR system. To overcome the above limitations, we propose a new SVM-based RF approach using probabilistic feature and weighted kernel function in this paper. Firstly, the probabilistic features of each image are extracted by using principal components analysis (PCA) and the adapted Gaussian mixture models (AGMM) based dimension reduction, and the similarity is computed by employing Kullback–Leibler divergence. Secondly, the positive feedback samples and negative feedback samples are marked, and all feedback samples’ weight values are computed by utilizing the samples-based Relief feature weighting. Finally, the SVM kernel function is modified dynamically according to the feedback samples’ weight values. Extensive simulations on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches.