Saturday, December 27, 2008

A new section of the Compact Composite Descriptors (CCD Layer 2) is now ready.

Two new papers will be presented at The Sixth IASTED International Conference on Signal Processing, Pattern Recognition and Applications ~SPPRA 2009~.

In the first paper we present a new low level compact composite descriptor for Content Based Medical Image Retrieval.
Abstract: The rapid advances made in the field of radiology, the increased frequency in which oncological diseases appear, as well as the demand for prevailing medical checks, led to the creation of a large database of radiology images in every hospital or medical center. There is now an imperative need to create an effective method for the indexing and retrieval of these images. This paper proposes a new method for content based medical image retrieval. The description of images relies on a new Composite Descriptor (CD) which includes global image features, capturing both brightness and texture characteristics at the same time. Image information is extracted using a set of fuzzy approaches. To be applicable in the design of large medical image databases, the proposed descriptor is compact, requiring only 48 bytes per image. Experiments demonstrate the effectiveness of the proposed technique. Authors: Savvas A. Chatzichristofis and Yiannis Boutalis.

The second paper is presenting a method for auto selection the proper compact composite descriptor in order to retrieve natural color images.
Abstract: Compact Composite Descriptors (CCD) are global image features capturing both, color and texture characteristics, at the same time in a very compact representation. In this paper we propose a combination of two recently introduced CCDs (CEDD and FCTH) into a Joint Composite Descriptor (JCD). We further present a method for descriptor selection to approach the best ANMRR that would result from CEDD and FCTH. With our approach the most appropriate descriptor in terms of maximization of information content can be found on a per image basis without knowledge of the data set as a whole. Experiments conducted on three known benchmarking image databases demonstrate the effectiveness of the proposed technique. Authors: Savvas A. Chatzichristofis, Mathias Lux and Yiannis Boutalis.

The descriptors will be added soon in the CCD section.

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