Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on On...
Chengcui Zhang, Xin Chen, Min Chen, Shu-Ching Chen...
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support ...
The automatic computation of features for content-based image retrieval still has difficulties to represent the concepts the user has in mind. Whenever an additional learning stra...
This paper presents a new framework that integrates relevance feedback into region-based image retrieval (RBIR) systems based on radial basis function network (RBFN). A modified u...
Learning the user’s semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relatio...