Content-based image retrieval using feature weighting and C-means clustering in a multi-label classification framework

In this paper, a novel learning algorithm based on feature weighting is proposed to improve the performance of image classification or retrieval systems in a multi-label framework. The goal is to exploit maximally the beneficial properties of each feature in the system. Since each feature can separate more effectively some of the image classes, it is hypothesized that the weights of various features at some states can be traded off against each other. The training phase of the suggested algorithm is performed in two stages: (1) The input images are clustered using a supervised C-means method iteratively; (2) image features are weighted using a local feature weighting method in each cluster. These weights are determined by considering the importance of each feature in minimizing the classification error on each cluster. In the testing phase, the cluster corresponding to the query is found first. Then, the most similar images are retrieved in the multi-label framework using the feature weights assigned to that cluster. Experimental results on three well-known, public and international image datasets demonstrate that our proposed method leads to significant performance gains over existing methods.