In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques i...
Haiming Liu 0002, Victoria S. Uren, Dawei Song, St...
Abstract. This paper presents an interactive content-based image retrieval framework--uInteract, for delivering a novel four-factor user interaction model visually. The four-factor...
Haiming Liu 0002, Srdan Zagorac, Victoria S. Uren,...
Content-Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many system...
A neural network scheme is presented in this paper for adaptive video indexing and retrieval. First, a limited but characteristic amount of frames are extracted from each video sc...
Nikolaos D. Doulamis, Anastasios D. Doulamis, Stef...
Relevance feedback [1] has been a powerful tool for interactive Content-Based Image Retrieval (CBIR). During the retrieval process, the user selects the most relevant images and p...