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In this paper, we describe an approach for the automatic medical annotation task of the 2008 CLEF cross-language image retrieval campaign (ImageCLEF). The data comprise 12076 fully annotated images according to the IRMA code. This work is focused on the process of feature extraction from images and hierarchical multi-label classification. To extract(More)
a r t i c l e i n f o This paper presents a hierarchical multi-label classification (HMC) system for diatom image classification. HMC is a variant of classification where an instance may belong to multiple classes at the same time and these classes/labels are organized in a hierarchy. Our approach to HMC exploits the classification hierarchy by building a(More)
Predictive models make predictions about values of data using known results from different data, while frequent itemsets describe properties of a subset of the data and are descriptive in nature. In this paper we present a method of building predictive models by using frequency information from frequent itemsets. Modifications were done on three standard(More)
This article is concerned with the classes of the Constraint Solving Engine and a Constraint Programming Library for problems that can be defined as Constraint Satisfaction Problems. The theoretical and mathematical foundations of our ideas and the problem solving process are explained. Among the first problems that were solved was the Traveling Salesman(More)
In this paper, we present the approach that we applied to the medical modality classification tasks at the ImageCLEF evaluation forum. More specifically, we used the modality classification databases from the ImageCLEF competitions in 2011, 2012 and 2013, described by four visual and one textual types of features, and combinations thereof. We used local(More)
This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in Image-CLEF 2013 medical tasks (modality classification, ad-hoc image retrieval and case-based retrieval). For the modality classification task we used SIFT descriptors and tf − idf weights of the surrounding text (image caption and(More)
Mammography image classification is a very important research field due to its domain of implementation. The aim of this paper is to compare feature extraction methods and to test them on a variety of classifiers. Five feature extraction methods were used: LBP, GLDM, GLRLM, Haralick and Gabor texture features. Three classification algorithms were used(More)
The cell functions and development are regulated by complex networks of genes, proteins and other components by means of their mutual interactions. These networks are called gene regulatory networks (GRNs). GRNs are used to reveal the fundamental gene regulatory mechanisms, to determine the reasons for many diseases and interactions between drugs and their(More)
In this paper, we present a hierarchical multi-label classification system for visual concepts detection and image annotation. Hierarchical multi-label classification (HMLC) is a variant of classification where an instance may belong to multiple classes at the same time and these classes/labels are organized in a hierarchy. The system is composed of two(More)