NDLI: kNN based image classification relying on local feature similarity

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kNN based image classification relying on local feature similarity

Content Provider	ACM Digital Library
Author	Amato, Giuseppe Falchi, Fabrizio
Abstract	In this paper, we propose a novel image classification approach, derived from the kNN classification strategy, that is particularly suited to be used when classifying images described by local features. Our proposal relies on the possibility of performing similarity search between image local features. With the use of local features generated over interest points, we revised the single label kNN classification approach to consider similarity between local features of the images in the training set rather than similarity between images, opening up new opportunities to investigate more efficient and effective strategies. We will see that classifying at the level of local features we can exploit global information contained in the training set, which cannot be used when classifying only at the level of entire images, as for instance the effect of local feature cleaning strategies. We perform several experiments by testing the proposed approach with different types of image local features in a touristic landmarks recognition task.
Starting Page	101
Ending Page	108
Page Count	8
File Format	PDF
ISBN	9781450304207
DOI	10.1145/1862344.1862360
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-09-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Landmarks Local features Image indexing Recognition Image classification
Content Type	Text
Resource Type	Article

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