Number of items: 3.
Li, Lusong and
Mei, Tao and
Liu, Chris and
Hua, Xian-Sheng GameSense. This paper presents a novel game-like advertising system called GameSense, which is driven by the compelling contents of online images. Given a Web page which typically contains images, GameSense is able to select suitable images to create online in-image games for advertising. The contextually relevant ads (i.e., product logos) are embedded at appropriate positions within the online games. The ads are selected based on not only textual relevance but also visual content similarity. The game is able to provide viewers rich experience and thus promote the embedded ads to provide more effective advertising.
Wu, Lei and
Yang, Linjun and
Yu, Nenghai and
Hua, Xian-Sheng Learning to Tag. Social tagging provides valuable and crucial information for large-scale web image retrieval. It is ontology-free and easy to obtain; however, irrelevant tags frequently appear, and users typically will not tag all semantic objects in the image, which is also called semantic loss. To avoid noises and compensate for the semantic loss, tag recommendation is proposed in literature. However, current recommendation simply ranks the related tags based on the single modality of tag co-occurrence on the whole dataset, which ignores other modalities, such as visual correlation. This paper proposes a multi-modality recommendation based on both tag and visual correlation, and formulates the tag recommendation as a learning problem. Each modality is used to generate a ranking feature, and Rankboost algorithm is applied to learn an optimal combination of these ranking features from different modalities. Experiments on Flickr data demonstrate the effectiveness of this learning-based multi-modality recommendation strategy.
Liu, Dong and
Hua, Xian-Sheng and
Yang, Linjun and
Wang, Meng and
Zhang, Hong-Jiang Tag Ranking. Social media sharing web sites like Flickr allow users to annotate images with free tags, which significantly facilitate Web image search and organization. However, the tags associated with an image generally are in a random order without any importance or relevance information, which limits the effectiveness of these tags in search and other applications. In this paper, we propose a tag ranking scheme, aiming to automatically rank the tags associated with a given image according to their relevance to the image content. We first estimate initial relevance scores for the tags based on probability density estimation, and then perform a random walk over a tag similarity graph to refine the relevance scores. Experimental results on a 50, 000 Flickr photo collection show that the proposed tag ranking method is both effective and efficient. We also apply tag ranking into three applications: (1) tag-based image search, (2) tag recommendation, and (3) group recommendation, which demonstrates that the proposed tag ranking approach really boosts the performances of social-tagging related applications.
This list was generated on Fri Feb 15 08:45:23 2019 GMT.
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