Image annotations allow users to access a large image database with textual queries. There have been several studies on automatic image annotation utilizing machine learning techn...
We propose a new method to measure “visualness” of concepts, that is, what extent concepts have visual characteristics. To know which concept has visually discriminative power...
Image annotation has been an active research topic in recent years due to its potentially large impact on both image understanding and Web image search. In this paper, we target a...
Xirong Li, Le Chen, Lei Zhang, Fuzong Lin, Wei-Yin...
Automatic keyword annotation is a promising solution to enable more effective image search by using keywords. In this paper, we propose a novel automatic image annotation method b...
With the prevalence of digital cameras, more and more people have considerable digital images on their personal devices. As a result, there are increasing needs to effectively sea...
Changhu Wang, Feng Jing, Lei Zhang, HongJiang Zhan...
Abstract. In order to evaluate image annotation and object categorisation algorithms, ground truth in the form of a set of images correctly annotated with text describing each imag...
Content-based image retrieval (CBIR) is currently limited because of the lack of representational power of the low-level image features, which fail to properly represent the actual...
This paper describes an efficient approach to image annotation. It ranked first on the recent scene categorization track of the ImagEVAL1 benchmark. We show how homogeneous globa...
Automatic image annotation automatically labels image content with semantic keywords. For instance, the Relevance Model estimates the joint probability of the keyword and the imag...
Xiangdong Zhou, Mei Wang, Qi Zhang, Junqi Zhang, B...
This paper investigates the problem of learning the visual semantics of keyword categories for automatic image annotation. Supervised learning algorithms which learn only a single ...