Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
Query-by-example is the most popular query model for today’s image retrieval systems. A typical query image contains not only relevant objects (e.g., Eiffel Tower), but also ir...
Benchmark tests have been designed and conducted for the purpose of evaluating the use of automated tape libraries in on-line digital check image retrieval applications. This type...
This paper describes a content-based image retrieval system that employs both higher-level and lower-level vision methodologies separately and in conjunction for the retrieval of ...
In this paper, we present a novel approach to contentsbased image retrieval. The method hinges in the use of quasi-random sampling to retrieve those images in a database which are...