Marcin Detyniecki

Ranking fuzzy numbers using ω-weighted valuations

We studied here on some simple examples the interaction between valuation family, parameters and ranking result. The ranking method studied is based upon the idea of associating with a fuzzy number a scalar value, its valuation, and using this valuation to compare and order fuzzy numbers. The valuation method considered was introduced initially by the Yager and Filev. This valuation consists in the integration over α-levels, of the average of each α-cut weighted by a weight distribution function. We finish by introducing a new weight distribution function.

A context-dependent method for ordering fuzzy numbers using probabilites

In this work we suggest an approach to comparing fuzzy numbers motivated by a probabilistic view of the underlying uncertainty. An important aspect of the method suggested is its context dependency, the numbers being compared affect the process. We discuss the effect of decision attitude and show that this approach is particularly useful for aiding decision makers having a temperate decision attitude, optimism in the face of adversity and conservatism in the face plenty.

Mobile museum guide based on fast SIFT recognition

This article explores the feasibility of a market-ready, mobile pattern recognition system based on the latest findings in the field of object recognition and currently available hardware and network technology. More precisely, an innovative, mobile museum guide system is presented, which enables camera phones to recognize paintings in art galleries. After careful examination, the algorithms Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) were found most promising for this goal. Consequently, both have been integrated in a fully implemented prototype system and their performance has been thoroughly evaluated under realistic conditions. In order to speed up the matching process for finding the corresponding sample in the feature database, an approximation to Nearest Neighbor Search was investigated. The k-means based clustering approach was found to significantly improve the computational time.

Adaptive Multimedia Retrieval: User, Context, and Feedback

Ontology-Based Retrieval and Annotation.- A Method for Processing the Natural Language Query in Ontology-Based Image Retrieval System.- SAFIRE: Towards Standardized Semantic Rich Image Annotation.- Ontology-Supported Video Modeling and Retrieval.- Ranking and Similarity Measurements.- Learning to Retrieve Images from Text Queries with a Discriminative Model.- A General Principled Method for Image Similarity Validation.- Rank-Test Similarity Measure Between Video Segments for Local Descriptors.- Music Information Retrieval.- Can Humans Benefit from Music Information Retrieval?.- Visual Modelling.- A New Approach to Probabilistic Image Modeling with Multidimensional Hidden Markov Models.- 3D Face Recognition by Modeling the Arrangement of Concave and Convex Regions.- Fuzzy Semantic Action and Color Characterization of Animation Movies in the Video Indexing Task Context.- Retrieval of Document Images Based on Page Layout Similarity.- Adaptive Retrieval.- Multimedia Content Adaptation Within the CAIN Framework Via Constraints Satisfaction and Optimization.- Aspects of Adaptivity in P2P Information Retrieval.- Interactive Museum Guide: Accurate Retrieval of Object Descriptions.- Structuring Multimedia.- Semantic Image Retrieval Using Region-Based Relevance Feedback.- Image Retrieval with Segmentation-Based Query.- Fast Structuring of Large Television Streams Using Program Guides.- User Integration and Profiling.- Variation of Relevance Assessments for Medical Image Retrieval.- An Efficient Collaborative Information Retrieval System by Incorporating the User Profile.- The Potential of User Feedback Through the Iterative Refining of Queries in an Image Retrieval System.

Visualizing changes in data collections using growing self-organizing maps

A. Nu/spl uml/rnberger (2001) has proposed a modification of the standard learning algorithm for self-organizing maps that iteratively increases the size of the map during the learning process by adding single neurons. The main advantage of this approach is the automatic control of the size and topology of the map, thus avoiding the problem of misclassification because of an imposed size. In this paper, we discuss how this algorithm can be used to visualize changes in data collections. We illustrate our approach with some examples.

Externally growing self-organizing maps and its application to e-mail database visualization and exploration

In this paper we present an approach to organize and classify e-mails using self-organizing maps. The aim is on the one hand to provide an intuitive visual profile of the considered mailing lists and on the other hand to offer an intuitive navigation tool, were similar e-mails are located close to each other, so that the user can scan easily for e-mails similar in content. To be able to evaluate this approach we have developed a prototypical software tool that imports messages from a mailing list and arranges/groups these e-mails based on a similarity measure. The tool combines conventional keyword search methods with a visualization of the considered e-mail collection. The prototype was developed based on externally growing self-organizing maps, which solve some problems of conventional self-organizing maps and which are computationally viable. Besides the underlying algorithms we present and discuss some system evaluations in order to show the capabilities of the approach.

Weighted self-organizing maps: incorporating user feedback

One interesting way of accessing collections of multimedia objects is by methods of visualization and clustering. Growing self-organizing maps provide such a solution, which adapts automatically to the underlying database. Unfortunately, the result of the clustering greatly depends on the definition of the describing features and the used similarity measure. In this paper, we present a general approach to improve the obtained clustering by incorporating user feedback (in the form of drag-and-drop) into the underlying topology of the self-organizing map.

A comparative study of diversity methods for hybrid text and image retrieval approaches

This article compares eight different diversity methods: 3 based on visual information, 1 based on date information, 3 adapted to each topic based on location and visual information; finally, for completeness, 1 based on random permutation. To compare the effectiveness of these methods, we apply them on 26 runs obtained with varied methods from different research teams and based on different modalities. We then discuss the results of the more than 200 obtained runs. The results show that query-adapted methods are more effcient than nonadapted method, that visual only runs are more difficult to diversify than text only and text-image runs, and finally that only few methods maximize both the precision and the cluster recall at 20 documents.

Exploiting Visual Concepts to Improve Text-Based Image Retrieval

In this paper, we study how to automatically exploit visual concepts in a text-based image retrieval task. First, we use Forest of Fuzzy Decision Trees (FFDTs) to automatically annotate images with visual concepts. Second, using optionally WordNet, we match visual concepts and textual query. Finally, we filter the text-based image retrieval result list using the FFDTs. This study is performed in the context of two tasks of the CLEF2008 international campaign: the Visual Concept Detection Task (VCDT) (17 visual concepts) and the photographic retrieval task (ImageCLEFphoto) (39 queries and 20k images). Our best VCDT run is the 4th best of the 53 submitted runs. The ImageCLEFphoto results show that there is a clear improvement, in terms of precision at 20, when using the visual concepts explicitly appearing in the query.

Real-World Fuzzy Logic Applications in Data Mining and Information Retrieval

This chapter focuses on real-world applications of fuzzy techniques for information retrieval and data mining. It gives a presentation of the theoretical background common to all applications, lying on two main elements: the concept of similarity and the fuzzy machine learning framework. It then describes a panel of real-world applications covering several domains namely medical, educational, chemical and multimedia.