An object-oriented approach for semantic-based image retrieval is presented. The goal is to identify key patterns of specific objects in the training data and to use them as object signature. Two important aspects of semantic-based image retrieval are considered: retrieval of images containing a given semantic concept and fusion of different low-level features. The proposed approach splits the ...

We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to data and demonstrate its effectiveness via asymptotic analysis and synthetic ex...

This paper presents a novel method for detecting masses in mammograms. Both topological (salience) and geometric (primarily texture) are used as features to characterise. Experimental results demonstrate that this combination of features is robust both for the segmentation and for the identification of masses.

Wikipedia is an online encyclopedia which anyone can edit. While most edits are constructive, about 7% are acts of vandalism. Such behavior is characterized by modifications made in bad faith; introducing spam and other inappropriate content. In this work, we present the results of an effort to integrate three of the leading approaches to Wikipedia vandalism detection: a spatio-temporal analysi...

In recent years, we proposed a number of new and very effective features for automatic writer identification and verification. They are probability distribution functions (PDFs) extracted from the handwriting images and characterize writer individuality independently of the textual content of the written samples. In this paper, we perform an extensive analysis of feature combinations. In our fu...

Combining multiple estimators to obtain a more accurate final result is a well-known technique in statistics. In the domain of speech recognition, there are many ways in which this general principle can be applied. We have looked at several ways for combining the information from different feature representations, and used these results in the best-performing system in last year’s Aurora evalua...

This paper presents a novel local feature selection approach for text categorization. It constructs a feature set for each category by first selecting a set of terms highly indicative of membership as well as another set of terms highly indicative of non-membership, then unifying the two sets. The size ratio of the two sets was empirically chosen to obtain optimal performance. This is in contra...

As multiword expressions (MWEs) exhibit a range of idiosyncrasies, their automatic detection warrants the use of many different features. Tsvetkov and Wintner (2014) proposed a Bayesian network model that combines linguistically motivated features and also models their interactions. In this paper, we extend their model with new features and apply it to Croatian, a morphologically complex and a ...

Scene classification is a useful, yet challenging problem in computer vision. Two important tasks for scene classification are the image representation and the choice of the classifier used for decision making. This paper proposes a new technique for scene classification using combined classifiers method. We run two classifiers based on different features: GistCMCT and spatial MCT and combine t...

This paper presents the approaches used by the MIRACLE team to image retrieval at ImageCLEF 2005. Text-based and content-based techniques have been tested, along with combination of both types of methods to improve image retrieval. The text-based experiments defined this year try to use semantic information sources, like thesaurus with semantic data or text structure. On the other hand, content...