Multiple-instance learning based decision neural networks for image retrieval and classification

The revolutionary Internet and digital technologies have spawned a need for technology that can organize abundantly available digital images for easy categorization and retrieval. Hence, content-based image retrieval (CBIR) has become one of the most active research areas for the last few decades. However, it is still an open issue to narrow down the gap between the high level semantics in the human minds and the low level features computable by machines. This paper proposes a multiple-instance learning based decision neural network (MI-BDNN) that attempts to bridge the semantic ga in CBIR. Multiple-instance learning (MIL) is a variation of supervised learning, where the training set is composed of many bags, and each bag contains many instances. If a bag contains at least one positive instance, it is labelled as a positive bag; otherwise, it is labelled as a negative bag. A novel discriminant function and learning schemes are employed in the MI-BDNN to learn the concept from the training bags. The proposed approach considers the image retrieval problem as a MIL problem, where a user׳s preferred image concept is learned by training MI-BDNN with a set of exemplar images, each of which is labelled as conceptual related (positive) or conceptual unrelated (negative) image. The MI-BDNN based CBIR system is developed, and the results of the experiments showed that MI-BDNN can successfully be used for real image retrieval and classification problems.