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...
Abstract. Understanding relationships and commonalities between digital contents based on metadata is a difficult user task that requires sophisticated presentation forms. In this ...
Steffen Lohmann, Philipp Heim, Lena Tetzlaff, Thom...
We propose a semi-supervised model which segments and annotates images using very few labeled images and a large unaligned text corpus to relate image regions to text labels. Give...
In this paper, we discuss meetings as an application domain for multimedia content analysis. Meeting databases are a rich data source suitable for a variety of audio, visual and m...
This paper presents a new spatial-HMM(SHMM)for automatically classifying and annotating natural images. Our model is a 2D generalization of the traditional HMM in the sense that b...