Detection-by-Localization: Maintenance-Free Change Object Detector

Recent researches demonstrate that self-localization performance is a very useful measure of likelihood-of-change (LoC) for change detection. In this paper, this "detection-by-localization" scheme is studied in a novel generalized task of object-level change detection. In our framework, a given query image is segmented into object-level subimages (termed "scene parts"), which are then converted to subimage-level pixel-wise LoC maps via the detection-by-localization scheme. Our approach models a self-localization system as a ranking function, outputting a ranked list of reference images, without requiring relevance score. Thanks to this new setting, we can generalize our approach to a broad class of self-localization systems. Our ranking based self-localization model allows to fuse self-localization results from different modalities via an unsupervised rank fusion derived from a field of multi-modal information retrieval (MMR).Comment: 7 pages, 3 figures, Technical repor

Combinação de métodos para pesquisa de informação

Pretende-se investigar diversos caminhos para combinar métodos de pesquisa por forma a melhorar o desempenho dos sistemas, oferecendo uma nova perspectiva da investigação dos sistemas de pesquisa, à descoberta da melhor estratégia, propondo um método de combinação baseado na combinação de três modelos: Textual, ligações e de classificação.info:eu-repo/semantics/publishedVersio

GeoTextMESS: result fusion with fuzzy Borda ranking in geographical information retrieval

In this paper we discuss the integration of different GIR systems by means of a fuzzy Borda method for result fusion. Two of the systems, the one by the Universidad Politécnica de Valencia and the one of the Universidad of Jaén participated to the GeoCLEF task under the name TextMess. The proposed result fusion method takes as input the document lists returned by the different systems and returns a document list where the documents are ranked according to the fuzzy Borda voting scheme. The obtained results show that the fusion method allows to improve the results of the component systems, although the fusion is not optimal, because it is effective only if the components return a similar set of relevant documents.Peer ReviewedPostprint (author’s final draft

Fusion of Text and Image in Multimedia Information Retrieval System

Multimedia Information Retrieval is very useful for any application in our daily work. Most of the applications consist of Multimedia data that are images, text, audio and video. Multimedia information retrieval system is used to search an image. There are same meanings for different data which is also known as semantic gap. This problem is solved by fusion of text based image retrieval and content based image retrieval. Weighted Mean, OWA and WOWA are aggregation operators used in this system for the fusion of text and image numeric values. The Scale invariant feature transforms and speeded up robust feature are two algorithms for feature extraction. To increase the speed of system, the speeded up robust feature algorithm is used. Bag of Words and Bag of Visual Word approaches are used in this system for retrieving images. DOI: 10.17762/ijritcc2321-8169.15066

The Analysis of Rank Fusion Techniques to Improve Query Relevance

Rank fusion meta-search engine algorithms can be used to merge web search results of multiple search engines. In this paper we introduce two variants of the Weighted Borda-Fuse algorithm. The first variant retrieves documents based on popularities of component engines. The second one is based on k user-defined toplist of component engines. In this research, experiments were performed on k={50,100,200} toplist with AND/OR combinations implemented on ‘UNIB Meta Fusion’ meta-search engine prototype which employed 3 out of 5 popular search engines. Both of our two algorithms outperformed other rank fusion algorithms (relevance score is upto 0.76 compare to Google that is 0.27, at P@10). The pseudo-relevance automatic judgement techniques involved are Reciprocal Rank, Borda Count, and Condorcet. The optimal setting was reached for queries with operator "AND" (degree 1) or "AND ... AND" (degree 2) with k=200. The ‘UNIB Meta Fusion’ meta-search engine system was built correctly

Suggestion contextuelle composite

International audienceLa suggestion contextuelle consiste à recommander à un utilisateur un ensemble de lieux d'activités adaptés à ses préférences et à son contexte. La plupart des approches existantes considèrent uniquement ces deux caractéristiques pour constituer leur liste de suggestions. Cependant, les recherches en systèmes de recommandation ont récemment souligné l'importance de la diversité des suggestions. Cet article présente un modèle novateur de suggestion contextuelle inspiré de la recherche composite qui consiste à regrouper les suggestions en différentes grappes thématiquement cohésives. L'évaluation réalisée dans le cadre de la piste Contextual Suggestion de TREC 2013 et 2014 montre que notre approche est compétitive et permet d'améliorer la diversité des suggestions sans dégrader leur pertinence