Ivica Dimitrovski

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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)
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)
In this paper, we present a novel deep learning architecture for sentiment analysis in Twitter messages. Our system finki, employs both convolutional and gated recurrent neural networks to obtain a more diverse tweet representation. The network is trained on top of GloVe word embeddings pre-trained on the Common Crawl dataset. Both neural networks are used(More)
In this paper we depict an implemented system for medical image retrieval. Our system performs retrieval based on both textual and visual content, separately and combined, using advanced encoding and quantization techniques. The text-based retrieval subsystem uses textual data acquired from an image’s corresponding article to generate a suitable(More)
This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in ImageCLEF 2012 medical retrieval task. We investigated by evaluating different weighting models for text retrieval. In the case of the visual retrieval, we focused on extracting low-level features and examining their performance. For,(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)
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)