In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between ...
Luo Si, Rong Jin, Steven C. H. Hoi, Michael R. Lyu
In this paper, an optimization based learning method is proposed for image retrieval from graph model point of view. Firstly, image retrieval is formulated as a regularized optimi...
We address the challenge of semantic gap reduction for image retrieval through an improved SVM-based active relevance feedback framework, together with a hybrid visual and concept...
The problem of search and retrieval of images using relevance feedback has attracted tremendous attention in recent years from the research community. A real-world-deployable inte...
Pradhee Tandon, Piyush Nigam, Vikram Pudi, C. V. J...
Traditional content-based image retrieval (CBIR) systems often fail to meet a user's need due to the `semantic gap' between the extracted features of the systems and the...