Abstract—Feature extraction is a key issue in contentbased image retrieval (CBIR). In the past, a number of texture features have been proposed in literature, including statistic...
In this paper, we extend the work done by Choubey and Raghavan, which proposed an approach to content-based image retrieval that uses the space transformation methods proposed by G...
Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
One of the major problems in CBIR is the so-called `semantic gap': the difference between low-level features, extracted from images, and the high-level `information need'...
Walter ten Brinke, David McG. Squire, John Bigelow
We consider the use of top-points for object retrieval. These points are based on scale-space and catastrophe theory, and are invariant under gray value scaling and offset as well ...