Content-based image retrieval uses features that can be extracted from the images themselves. Using more than one representation of the images in a collection can improve the resu...
Content-Based Image Retrieval (CBIR) systems have been developed aiming at enabling users to search and retrieve images based on their properties such as shape, color and texture....
Ricardo da Silva Torres, Eduardo M. Picado, Alexan...
In this paper, we will show how non-photorealistic rendering (NPR) can take a new role in content-based image retrieval (CBIR). We propose a content-based image retrieval method. ...
: Modeling the characteristics of specific images and individual users is a critical issue in content-based image retrieval but insufficiently addressed by the current retrieval ap...
In multi-instance learning, the training examples are bags composed of instances without labels and the task is to predict the labels of unseen bags through analyzing the training...
We have been developing new relevance feedback algorithms for Content-based Image Retrieval (CBIR) that allow the user to achieve more flexible query. In conjunction with the new...
Munehiro Nakazato, Charlie K. Dagli, Thomas S. Hua...
Concept learning in content-based image retrieval (CBIR) systems is a challenging task. This paper presents an active concept learning approach based on mixture model to deal with...
The performance of a Content-Based Image Retrieval (CBIR) system presented in the form of Precision-Recall or PrecisionScope graphs offers an incomplete overview of the system und...
Nicu Sebe, Dionysius P. Huijsmans, Qi Tian, Theo G...
Many different approaches for content-based image retrieval have been proposed in the literature. Successful approaches consider not only simple features like color, but also take ...
Karin Kailing, Hans-Peter Kriegel, Stefan Schö...
Thermal medical imaging provides a valuable method for detecting various diseases such as breast cancer or Raynaud’s syndrome. While previous efforts on the automated processing...