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...
We propose an unsupervised approach to learn associations between continuous-valued attributes from different modalities. These associations are used to construct a multi-modal t...
To bridge the semantic gap in content-based image retrieval, detecting meaningful visual entities (e.g. faces, sky, foliage, buildings etc) in image content and classifying images...
Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image ...
As tools and systems for producing and disseminating image data have improved significantly in recent years, the volume of digital images has grown rapidly. An efficient mechanism ...