Context-aware image semantic extraction in the social web
Proceedings of the 21st international conference companion on World Wide Web
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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.