This paper proposes a content-based medical image retrieval (CBMIR) framework using dynamically optimized features from multiple regions of medical images. These regional features...
Wei Xiong, Bo Qiu, Qi Tian, Changsheng Xu, Sim Hen...
Inaccurate or ambiguous expressions in queries lead to poor results in information retrieval. We assume that iterative user feedback can improve the quality of queries. To this end...
Maher Ben Moussa, Marco Pasch, Djoerd Hiemstra, Pa...
This paper presents an effective fuzzy long-term semantic learning method for relevance feedback-based image retrieval. The proposed system uses a statistical correlationbased met...
This paper presents a medical image retrieval framework that uses visual concepts in a feature space employing statistical models built using a probabilistic multi-class support v...
Md. Mahmudur Rahman, Sameer Antani, George R. Thom...
Motivated by the need to efficiently leverage user relevance feedback in content-based retrieval from image databases, we propose a fast, clustering-based indexing technique for e...