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Istituto di Scienza e Tecnologie dell'Informazione     
Amato G., Falchi F., Gennaro C. Geometric consistency checks for kNN based image classification relying on local features. In: SISAP 2011 - Fourth International Conference on SImilarity Search and APplications (Lipari, Italia, 30 Giugno - 1 Luglio 2011). Proceedings, pp. 81 - 88. Alfredo Ferro. ACM, 2011.
 
 
Abstract
(English)
Applications of image content recognition, as for instance landmark recognition, can be obtained by using techniques of kNN classifications based on the use of local image features, such as SIFT or SURF. Quality of image classification can be improved by defining geometric consistency check rules based on space transformations of the scene depicted in images. However, this prevents the use of state of the art access methods for similarity searching and sequential scan of the images in the training sets has to be executed in order to perform classification. In this paper we propose a technique that allows one to use access methods for similarity searching, such as those exploiting metric space properties, in order to perform kNN classification with geometric consistency checks. We will see that the proposed approach, in addition to offer an obvious efficiency improvement, surprisingly offers also an improvement of the effectiveness of the classification.
URL: http://dl.acm.org/citation.cfm?id=1995428
DOI: 10.1145/1995412.1995428
Subject image classification
image indexing
recognition
similaritys earch
local features
H.3.1 Content Analysis and Indexing
H.3.3 Information Search and Retrieval


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