Sammendrag
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
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