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    Travelogue enriching and scenic spot overview based on textual and visual topic models

    Pang, Y. and Lu, X. and Yuan, Y. and Li, Xuelong (2011) Travelogue enriching and scenic spot overview based on textual and visual topic models. International Journal of Pattern Recognition and Artificial Intelligence 25 (03), pp. 373-390. ISSN 0218-0014.

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    We consider the problem of enriching the travelogue associated with a small number (even one) of images with more web images. Images associated with the travelogue always consist of the content and the style of textual information. Relying on this assumption, in this paper, we present a framework of travelogue enriching, exploiting both textual and visual information generated by different users. The framework aims to select the most relevant images from automatically collected candidate image set to enrich the given travelogue, and form a comprehensive overview of the scenic spot. To do these, we propose to build two-layer probabilistic models, i.e. a text-layer model and image-layer models, on offline collected travelogues and images. Each topic (e.g. Sea, Mountain, Historical Sites) in the text-layer model is followed by an image-layer model with sub-topics learnt (e.g. the topic of sea is with the sub-topic like beach, tree, sunrise and sunset). Based on the model, we develop strategies to enrich travelogues in the following steps: (1) remove noisy names of scenic spots from travelogues; (2) generate queries to automatically gather candidate image set; (3) select images to enrich the travelogue; and (4) choose images to portray the visual content of a scenic spot. Experimental results on Chinese travelogues demonstrate the potential of the proposed approach on tasks of travelogue enrichment and the corresponding scenic spot illustration.


    Item Type: Article
    Keyword(s) / Subject(s): image retrieval, probabilistic model, text mining, travelogue, user-generated content
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 07 Jun 2013 13:58
    Last Modified: 11 Oct 2016 15:27


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