Abstract
Traditional CBIR method relies on visual features to identify objects in an image and uses predefined terms to annotate images, thus it fails to depict the implicit meanings. Recent textual content analysis methods applied to image annotation were blamed for their complexity of computation. In this research, we propose a corpus-free, relatively light computation of term segmentation method, namely “Iterative Merging Chinese Segmentation (IMCS) ,” to identify representative terms from a single web page to obtain anecdotes as a semantic enrichment of the target image. It requires minimum computation needs that allows to share characters/words and facilitate their use at fine granularities without prohibitive cost. In the experiment, this method achieves a precision rate of 86.02%, and gains acceptance from expert rating and user rating of 75% and 68%, respectively. In performance testing, it only takes 0.006 second to process each image in a collection of 1,728 testing data set.
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Huang, CM., Chang, YJ. (2013). Applying a Lightweight Iterative Merging Chinese Segmentation in Web Image Annotation. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_14
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DOI: https://doi.org/10.1007/978-3-642-39712-7_14
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