In this paper, we introduce a new approach to learn dissimilarity for interactive search in content based image retrieval. In literature, dissimilarity is often learned via the fe...
Giang P. Nguyen, Marcel Worring, Arnold W. M. Smeu...
Although relevance feedback has been extensively studied in content-based image retrieval in the academic area, no commercial web image search engine has employed the idea. There ...
En Cheng, Feng Jing, Mingjing Li, Wei-Ying Ma, Hai...
: Relevance feedback in Content Based Image Retrieval(CBIR) has been an active field of research for quite some time now. Many schemes and techniques of relevance feedback exist w...
Abstract. This paper presents a statistical framework based on Principal Component Analysis (PCA) for discovering the contextual factors which most strongly influence user behavio...
The paper describes our participation in Monolingual tasks at CLEF 2007. We submitted results for the following languages: Hungarian, Bulgarian and Czech. We focused on studying d...
The retrieval performance of content-based image retrieval (CBIR) systems is often disappointingly low, mainly due to the subjectivity of human perception. Relevance feedback (RF)...
Sotirios Chatzis, Anastasios D. Doulamis, Theodora...
Content-based image retrieval with relevant feedback has been widely adopted as the query model of choice for improved effectiveness in image retrieval. The effectiveness of thi...
In this project (VIRSI) we investigate the promising contentbased retrieval paradigm known as interactive search or relevance feedback, and aim to extend it through the use of syn...
Bart Thomee, Mark J. Huiskes, Erwin M. Bakker, Mic...
Traditional adaptive filtering systems learn the user’s interests in a rather simple way – words from relevant documents are favored in the query model, while words from irre...
The goal of this paper is to study the image-concept relationship as it pertains to image annotation. We demonstrate how automatic annotation of images can be implemented on partia...