Radon-Gabor Barcodes for Medical Image Retrieval

In recent years, with the explosion of digital images on the Web, content-based retrieval has emerged as a significant research area. Shapes, textures, edges and segments may play a key role in describing the content of an image. Radon and Gabor transforms are both powerful techniques that have been widely studied to extract shape-texture-based information. The combined Radon-Gabor features may be more robust against scale/rotation variations, presence of noise, and illumination changes. The objective of this paper is to harness the potentials of both Gabor and Radon transforms in order to introduce expressive binary features, called barcodes, for image annotation/tagging tasks. We propose two different techniques: Gabor-of-Radon-Image Barcodes (GRIBCs), and Guided-Radon-of-Gabor Barcodes (GRGBCs). For validation, we employ the IRMA x-ray dataset with 193 classes, containing 12,677 training images and 1,733 test images. A total error score as low as 322 and 330 were achieved for GRGBCs and GRIBCs, respectively. This corresponds to $\approx 81\%$ retrieval accuracy for the first hit.Comment: To appear in proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201

Gobernanza ambiental y actores sociales : hacia la articulación de saberes múltiples y diversos en la gobernanza ambiental

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Investigating bias in semantic similarity measures for analysis of protein interactions

Protein interactions are fundamental blocks of almost all cellular processes, so the study of the set of protein interactions in a single organism (also referred to as Protein Interaction Networks - PIN) is an important step in the comprehension of mechanism at molecular level. Recently, the possibility to annotate such data using Gene Ontology and the consequent use of ontology-based analysis has been exploited, e.g. the use of semantic similarity (SS) measures. Whereas, SS measures present many challenges and different issues that have to be faced. In particular SS measures are affected from three main biases: i) annotation length, ii) evidence codes, and iii) shallow annotation. The common cause of such biases are the structure of GO and the corpora of annotations (GOA). Consequently, the impact of this variability has to be considered when developing novel algorithms for protein interactions analysis. Although the criticality of these aspects, there is a lack in the systematic analysis of the bias. Few works dealt with the three sources of bias most affecting SS measures. This paper demonstrates the existence of the bias that affect main SS on a set of well-known yeast complexes. It also provides some evidences about the variability of the bias effects over the proteome