Object recognition in wikimage data based on local invariant image features
|Object recognition in wikimage data based on local invariant image features|
|Author(s)||Tomasev N., Pracner D., Brehar R., Radovanovic M., Mladenic D., Ivanovic M., Nedevschi S.|
|Published in||Proceedings - 2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing, ICCP 2013|
|Keyword(s)||classification, hubness, images, local invariant features, object recognition, WIKImage, Wikipedia (Extra: Hubness, images, Local invariant features, WIKImage, Wikipedia, Classification (of information), Communication, Content based retrieval, Topology, Object recognition)|
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Object recognition in wikimage data based on local invariant image features is a 2013 conference paper written in English by Tomasev N., Pracner D., Brehar R., Radovanovic M., Mladenic D., Ivanovic M., Nedevschi S. and published in Proceedings - 2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing, ICCP 2013.
Object recognition is an essential task in content-based image retrieval and classification. This paper deals with object recognition in WIKImage data, a collection of publicly available annotated Wikipedia images. WIKImage comprises a set of 14 binary classification problems with significant class imbalance. Our approach is based on using the local invariant image features and we have compared 3 standard and widely used feature types: SIFT, SURF and ORB. We have examined how the choice of representation affects the k-nearest neighbor data topology and have shown that some feature types might be more appropriate than others for this particular problem. In order to assess the difficulty of the data, we have evaluated 7 different k-nearest neighbor classification methods and shown that the recently proposed hubness-aware classifiers might be used to either increase the accuracy of prediction, or the macro-averaged F-score. However, our results indicate that further improvements are possible and that including the textual feature information might prove beneficial for system performance.
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