Solving multi-instance problems with classifier ensemble based on constructive clustering
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Journal of Systems and Software
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
Relevance feedback using adaptive clustering for region based image similarity retrieval
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Locating regions of interest in CBIR with multi-instance learning techniques
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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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 in.uence 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.