NDLI: Event Clusters Detection on Flickr Images Using a Suffix-Tree Structure

Content Provider	IEEE Xplore Digital Library
Author	Ruocco, M. Ramampiaro, H.
Copyright Year	2010
Abstract	Image clustering is a problem that has been treated extensively in both Content-Based (CBIR) and Text-Based (TBIR) Image Retrieval Systems. In this paper, we propose a new image clustering approach that takes both annotation, time and geographical position into account. Our goal is to develop a clustering method that allows an image to be part of an event cluster. We extend a well-known clustering algorithm called Suffix Tree Clustering (STC), which was originally developed to cluster text documents using a document snippet. To be able to use this algorithm, we consider an image with annotation as a document. Then, we extend it to also include time and geographical position. This appears to be particularly useful on the images gathered from online photo-sharing applications such as Flickr. Here image tags are often subjective and incomplete. For this reason, clustering based on textual annotations alone is not enough to capture all context information related to an image. Our approach has been suggested to address this challenge. In addition, we propose a novel algorithm to extract event clusters. The algorithm is evaluated using an annotated dataset from Flickr, and a comparison between different granularity of time and space is provided.
Starting Page	41
Ending Page	48
File Size	1397919
Page Count	8
File Format	PDF
ISBN	9781424486724
DOI	10.1109/ISM.2010.16
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-13
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Semantics Feature extraction Event detection Silicon Algorithm design and analysis Visualization Image Annotation Event Detection Image Clustering Event Clustering Suffix Tree
Content Type	Text
Resource Type	Article

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