1,721,215 research outputs found
Statistical and neural classifiers: an integrated approach to design (Advances in Pattern Recognition Series)
No full textAtti del Convegno su "Il Telerilevamento ed i Sistemi Informativi Territoriali nella Gestione delle Risorse Ambientali", Trento, Italia, 27 Ottobre 1994
No full textDecision-level fusion of PCA and LDA-based face recognition algorithms
No full textIn this paper, a face recognition system based on the fusion of two well-known appearance-based algorithms, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), is proposed. Fusion is performed at the decision-level, that is, the outputs of the individual face recognition algorithms are combined. Two main benefits of such fusion are shown. First, the reduction of the dependence on the environmental conditions with respect to the best individual recogniser. Secondly, the overall performance improvement over the best individual recogniser. To this end, fusion is investigated under different environmental conditions, namely, ``ideal'' conditions, characterised by a very limited variability of environmental parameters, and ``real'' conditions with large variability of lighting and face expressions
Liveness Detection Competition 2009
No full textThe widespread use of personal verification systems based on fingerprints has shown some security weaknesses. Gian Luca Marcialis, assistant professor at the Department of electrical and electronic engineering in the University of Cagliari reports on the first international fingerprint liveness detection competition 2009 – LivDet 2009
Instance-Based Relevance Feedback for Image Retrieval
No full textHigh retrieval precision in content-based image retrieval can be
attained by adopting relevance feedback mechanisms. These
mechanisms require that the user judges the quality of the results of
the query by marking all the retrieved images as being either
relevant or not. Then, the search engine exploits this information to
adapt the search to better meet user’s needs. At present, the vast
majority of proposed relevance feedback mechanisms are
formulated in terms of search model that has to be optimized. Such
an optimization involves the modification of some search
parameters so that the nearest neighbor of the query vector contains
the largest number of relevant images. In this paper, a different
approach to relevance feedback is proposed. After the user
provides the first feedback, following retrievals are not based on knn search, but on the computation of a relevance score for each
image of the database. This score is computed as a function of two
distances, namely the distance from the nearest non-relevant image
and the distance from the nearest relevant one. Images are then
ranked according to this score and the top k images are displayed.
Reported results on three image data sets show that the proposed
mechanism outperforms other state-of-the-art relevance feedback
mechanisms
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