Image annotation is a promising approach to bridging the semantic gap between low-level features and high-level concepts, and it can avoid the heavy manual labor. Most existing automatic image annotation approaches are based on supervised learning. They often encounter several problems, such as insufficiency of training data, lack of ability in dealing with new concept, and a limited number of ...

There has been a growing interest in implementing online Web image search engine in the semantic level. However, most existing practical systems including popular commercial Web image search engines like Google and Yahoo! are either textbased or a simple hybrid of texts and visual features. This paper proposes a novel Web Image Search by Automatic Image Annotation and Translation (WISAIAT) syst...

: Automatic annotation of Web image has great potential in improving the performance of web image retrieval. This paper presents a Broadcast Model (BM) for Web image annotation. In this model, pages are divided into blocks and the annotation of image is realized through the interaction of information from blocks and relevant web pages. Broadcast means each block will receive information (just l...

Image annotation is typically performed manually since automatic image annotation approaches have not matured yet to be used in practice. Consequently, image annotation is a labour intensive and time consuming task. In this paper, we show how an image browsing system can be employed to support efficient and effective (manual) annotation of image databases. In contrast to other approaches, which...

Abstract: Relevant past events can be remembered when visualizing related pictures. The main difficulty is how to find these photos in a large personal collection. Query definition and image annotation are key issues to overcome this problem. The former is relevant due to the diversity of the clues provided by our memory when recovering a past moment and the later because images need to be anno...

Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method that scales to such datasets by simultaneously learning to optimize precision at the top of the ranked list of annotations for a given image and learning a lowdimensional joint embedding space for both images and annotat...