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Author Amato, Giuseppe ♦ Falchi, Fabrizio ♦ Gennaro, Claudio
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Image classification ♦ KNN classification ♦ Local features ♦ Object recognition ♦ Tourism
Abstract Content-based image classification is a wide research field that addresses the landmark recognition problem. Among the many classification techniques proposed, the $\textit{k}-nearest$ neighbor $(\textit{kNN})$ is one of the most simple and widely used methods. In this article, we use $\textit{kNN}$ classification and landmark recognition techniques to address the problem of monument recognition in images. We propose two novel approaches that exploit $\textit{kNN}$ classification technique in conjunction with local visual descriptors. The first approach is based on a relaxed definition of the local feature based image to image similarity and allows standard $\textit{kNN}$ classification to be efficiently executed with the support of access methods for similarity search. The second approach uses $\textit{kNN}$ classification to classify local features rather than images. An image is classified evaluating the consensus among the classification of its local features. In this case, access methods for similarity search can be used to make the classification approach efficient. The proposed strategies were extensively tested and compared against other state-of-the-art alternatives in a monument and cultural heritage landmark recognition setting. The results proved the superiority of our approaches. An additional relevant contribution of this work is the exhaustive comparison of various types of local features and image matching solutions for recognition of monuments and cultural heritage related landmarks.
ISSN 15564673
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-08-01
Publisher Place New York
e-ISSN 15564711
Journal Journal on Computing and Cultural Heritage (JOCCH)
Volume Number 8
Issue Number 4
Page Count 25
Starting Page 1
Ending Page 25

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Source: ACM Digital Library