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Open AccessArticle

Image Collection Summarization Method Based on Semantic Hierarchies

1
Center for Biomedical Image Computing & Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
2
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran 15119-43943, Iran
*
Author to whom correspondence should be addressed.
AI 2020, 1(2), 209-228; https://doi.org/10.3390/ai1020014
Received: 14 April 2020 / Revised: 10 May 2020 / Accepted: 13 May 2020 / Published: 18 May 2020
The size of internet image collections is increasing drastically. As a result, new techniques are required to facilitate users in browsing, navigation, and summarization of these large volume collections. Image collection summarization methods present users with a set of exemplar images as the most representative ones from the initial image collection. In this study, an image collection summarization technique was introduced according to semantic hierarchies among them. In the proposed approach, images were mapped to the nodes of a pre-defined domain ontology. In this way, a semantic hierarchical classifier was used, which finally mapped images to different nodes of the ontology. We made a compromise between the degree of freedom of the classifier and the goodness of the summarization method. The summarization was done using a group of high-level features that provided a semantic measurement of information in images. Experimental outcomes indicated that the introduced image collection summarization method outperformed the recent techniques for the summarization of image collections. View Full-Text
Keywords: ontology; hierarchical classification; image collection summarization ontology; hierarchical classification; image collection summarization
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Riahi Samani, Z.; Ebrahimi Moghaddam, M. Image Collection Summarization Method Based on Semantic Hierarchies. AI 2020, 1, 209-228.

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