Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning

In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data. We derive s-ipr as a supervised extension of our previously proposed iterative projections and rotations (ipr) algorithm for incoherent dictionary learning, and we employ it to learn incoherent sub-spaces that model signals belonging to different classes. We test our method as a feature transform for supervised classification, first by visualising transformed features from a synthetic dataset and from the ‘iris’ dataset, then by using the resulting features in a classification experiment

Definitions in ontologies

Definitions vary according to context of use and target audience. They must be made relevant for each context to fulfill their cognitive and linguistic goals. This involves adapting their logical structure, type of content, and form to each context of use. We examine from these perspectives the case of definitions in ontologies

A Survey of Super-Resolution in Iris Biometrics With Evaluation of Dictionary-Learning

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches thus need to incorporate the specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an eigen-patches’ reconstruction method based on the principal component analysis eigen-transformation of local image patches. The structure of the iris is exploited by building a patch-position-dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded the high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators that were used to carry out biometric verification and identification experiments. The experimental results show that the proposed method significantly outperforms both the bilinear and bicubic interpolations at a very low resolution. The performance of a number of comparators attains an impressive equal error rate as low as 5% and a Top-1 accuracy of 77%–84% when considering the iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matchingThis work was supported by the EU COST Action under Grant IC1106. The work of F. Alonso-Fernandez and J. Bigun was supported in part by the Swedish Research Council, in part by the Swedish Innovation Agency, and in part by the Swedish Knowledge Foundation through the CAISR/SIDUS-AIR projects. The work of J. Fierrez was supported by the Spanish MINECO/FEDER through the CogniMetrics Project under Grant TEC2015-70627-R. The authors acknowledge the Halmstad University Library for its support with the open access fee

Ontology population for open-source intelligence: A GATE-based solution

Open-Source INTelligence is intelligence based on publicly available sources such as news sites, blogs, forums, etc. The Web is the primary source of information, but once data are crawled, they need to be interpreted and structured. Ontologies may play a crucial role in this process, but because of the vast amount of documents available, automatic mechanisms for their population are needed, starting from the crawled text. This paper presents an approach for the automatic population of predefined ontologies with data extracted from text and discusses the design and realization of a pipeline based on the General Architecture for Text Engineering system, which is interesting for both researchers and practitioners in the field. Some experimental results that are encouraging in terms of extracted correct instances of the ontology are also reported. Furthermore, the paper also describes an alternative approach and provides additional experiments for one of the phases of our pipeline, which requires the use of predefined dictionaries for relevant entities. Through such a variant, the manual workload required in this phase was reduced, still obtaining promising results

The functions of definitions in ontologies

To understand what ontologies do through their definitions, we propose a theoretical explanation of the functions of definitions in ontologies backed by empirical neuropsychological studies. Our goal is to show how these functions should motivate (i) the systematic inclusion of definitions in ontologies and (ii) the adaptation of definition content and form to the specific context of use of ontologies