Social media sharing Websites allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval. Tag-based image search is an important method to find images shared by users in social networks. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a topic diverse ranking approach for tag-based image retrieval with the consideration of promoting the topic coverage performance. First, we construct a tag graph based on the similarity between each tag. Then, the community detection method is conducted to mine the topic community of each tag. After that, inter-community and intra-community ranking are introduced to obtain the final retrieved results. In the inter-community ranking process, an adaptive random walk model is employed to rank the community based on the multi-information of each topic community. Besides, we build an inverted index structure for images to accelerate the searching process. Experimental results on Flickr data set and NUS-Wide data sets show the effectiveness of the proposed approach.

Social media sharing websites like Flickr allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval and organization. Tag-based image search is an important method to find images contributed by social users in such social websites. However, how to make the top ranked result relevant and, with diversity, is challenging. In this paper, we propose a social re-ranking system for tag-based image retrieval with the consideration of an image's relevance and diversity. We aim at re-ranking images according to their visual information, semantic information, and social clues. The initial results include images contributed by different social users. Usually each user contributes several images. First, we sort these images by inter-user re-ranking. Users that have higher contribution to the given query rank higher. Then we sequentially implement intra-user re-ranking on the ranked user's image set, and only the most relevant image from each user's image set is selected. These selected images compose the final retrieved results. We build an inverted index structure for the social image dataset to accelerate the searching process. Experimental results on a Flickr dataset show that our social re-ranking method is effective and efficient.

Due to the popularity of online video sharing Web sites such as YouTube, millions of users have treated online video as a source of information and entertainment. Therefore, video annotation has evoked great interest in the past few years. In this paper, we propose a four-step approach to automatically annotate video shots with sentences. The first step is video preprocessing, converting video shot into a sequence of frame images. The second step is to find related candidate elements of the sentence about the video contents. The main elements in the sentence are objects, events, scenes, and modifiers. These candidate elements are gained by searching for similar images with the video frames in our collected image data sets instead of video data sets. The third step is to select the best elements among these candidate ones by a weighted scoring algorithm. The final step is to construct a sentence with the help of a correlation graph algorithm to analyze the relationships among the best elements. The experimental results indicate that our method is effective to annotate videos with sentences. What is more, the weighted scoring algorithm and the correlation graph algorithm that we propose are efficient in developing the experimental performance.

Tag-based image search is an important method to process images contributed by social users in social media sharing websites like Flickr. However, existing ranking methods for tag-based image search frequently return results that are irrelevant, low-diversity or time-consuming. In this paper, we propose a user-oriented image ranking system with the consideration of image relevance, diversity and computation complexity, aiming to automatically rank images according to their visual information, semantic information and social clues. When you input a query in the user-oriented image search engine, images tagged with query are obtained as the initial results. The initial results include images contributed by different social users. Usually each user contributes several images. First we sort these users by inter-user ranking. Users that have a higher contribution to the given query rank higher. Then we sequentially implement intra-user ranking on the ranked user's image set, and only the most relevant image in each user's image set is selected. These selected images compose the final retrieval results. Experimental results on Flickr dataset show that our user-oriented ranking method is effective and efficient.

Social image share websites such as Flickr allow users to manually annotate their images with their own words, which can be used to facilitating image retrieval and other image applications. For the vast number of online images contributed by social users, existing methods on tag recommendation haven't taken users' characteristics and tagging habits into consideration. In this paper, we propose a personalized tag recommendation system for Flickr users. It can recommend users personalized tags for their newly uploaded photos based on the history information in their social communities. We carry out the personalized tag recommendation from three aspects. First, the tags we recommend to users are users' own vocabularies. Second, different recommendation methods are implemented to different users. Third, different users are recommended with different number of tags based on their tagging habits. The experimental results indicate that our personalized tag recommendation is effective.