We present a Bayesian framework for content-based image retrieval which models the distribution of color and texture features within sets of related images. Given a userspecified ...
In this demonstration, we present the Automatic Linguistic Indexing of Pictures (ALIP) system. The system annotates images with linguistic terms, chosen among hundreds of such ter...
Learning-enhanced relevance feedback is one of the most promising and active research directions in recent year's content-based image retrieval. However, the existing approac...
Color histogram is an important part of content-based image retrieval systems. It is a common understanding that histograms that adapt to images can represent their color distribu...
A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two parts. Im...
Guodong Guo, Anil K. Jain, Wei-Ying Ma, HongJiang ...
Online photo sharing systems, such as Flickr and Picasa, provide a valuable source of human-annotated photos. Textual annotations are used not only to describe the visual content ...
This paper makes two contributions. The first contribution is an approach called the "customized-queries" approach (CQA) to content-based image retrieval. The second is ...
Jennifer G. Dy, Carla E. Brodley, Avinash C. Kak, ...
We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effec...
Jing Huang, Ravi Kumar, Mandar Mitra, Wei-Jing Zhu...