In this paper, we present the results of our work that seek to negotiate the gap between low-level features and high-level concepts in the domain of web document retrieval. This wo...
In this paper, we propose a probabilistic model for web image mining, which is based on concept-sensitive salient regions without human intervene. Our goal is to achieve a middle-...
Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which ha...
Ondrej Chum, James Philbin, Josef Sivic, Michael I...
In this paper, we present a method of semantic knowledge building for image database by extracting semantic meanings from Web page contents. The novelty of our method is that it i...
Yung-Kwang Lai, Song Liu, Liang-Tien Chia, Syin Ch...
Existing Web image search engines index images by textual descriptions including filename, image caption, surrounding text, etc. However, the textual description available on the W...