: Despite the efforts to reduce the semantic gap between user perception of similarity and featurebased representation of images, user interaction is essential to improve retrieval...
This paper addresses the issue of effective and efficient content based image retrieval by presenting a novel indexing and retrieval methodology that integrates color, texture, an...
The main goal of content based image retrieval is to e ciently retrieve images that are visually similar to a query image. In this paper we will focus on content based image retri...
Content based image retrieval is an active research area of pattern recognition. A new method of extracting global texture energy descriptors is proposed and it is combined with fe...
We describe and compare three probabilistic ways to perform Content Based Image Retrieval (CBIR) in compressed domain using images in JPEG2000 format. Our main focus are arbitrary ...
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support ...
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on On...
Chengcui Zhang, Xin Chen, Min Chen, Shu-Ching Chen...
Content based image retrieval (CBIR), a technique which uses visual contents to search images from the large scale image databases, is an active area of research for the past decad...
Amit Jain, Ramanathan Muthuganapathy, Karthik Rama...
This paper describes the technical details of SemRetriev, a prototype system for image retrieval which combines the use of an ontology which structures an image repository and of ...
Most systems for content based image retrieval (CBIR) employ low level image features as a similarity measure. The problem of CBIR systems is that they are a “black box” to th...