A Novel Bag Generator for Image Database Retrieval With Multi-Instance Learning Techniques

In multi-instance learning, the training examples are bags composed of instances without labels and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. In content-based image retrieval (CBIR), the query is ambiguous because it is hard to ask the user precisely specify what he or she wants. Such kind of ambiguity can be gracefully dealt with by multi-instance learning techniques, and previous research shows that bag generators can significantly influence the performance of a CBIR system. In this paper, a novel bag generator named ImaBag is presented, where the pixels of each image are first clustered based on their color and spatial features and then the clustered blocks are merged and converted into a specific number of instances. Experiments show that ImaBag achieves comparable results to some existing bag generators but is more efficient in retrieving images from databases.