In this paper, we present a multimodal parallel text-image corpus, and propose an image annotation method that exploits the textual information associated with images. Our corpus ...
Several Automatic Image Annotation (AIA) algorithms have been introduced recently, which have been found to outperform previous models. However, each one of them has been evaluated...
This paper describes an application of statistical co-occurrence techniques that built on top of a probabilistic image annotation framework is able to increase the precision of an ...
Ainhoa Llorente, Simon E. Overell, Haiming Liu 000...
In this paper, we propose a novel Markov model-based formulation for the image annotation problem. In this formulation, we treat image annotation as a graph ranking problem, by de...
As the consequence of semantic gap, visual similarity does not guarantee semantic similarity, which in general is conflicting with the inherent assumption of many generativebased ...
Abstract. Automatic image annotation has been becoming an attractive research subject. Most current image annotation methods are based on training techniques. The major weaknesses ...
Automatic image annotation empowers the user to search an image database using keywords, which is often a more practical option than a query-by-example approach. In this work, we p...
In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning...
Libraries have traditionally used manual image annotation for indexing and then later retrieving their image collections. However, manual image annotation is an expensive and labo...
In this paper, we propose the use of the Maximum Entropy approach for the task of automatic image annotation. Given labeled training data, Maximum Entropy is a statistical techniqu...