Our approach locates the temporal positions of tags in videos at the keyframe level.We deal with a scenario in which there is no pre-defined set of tags.We report experiments about the use of different web sources (Flickr, Google, Bing).We show state-of-the-art results on DUT-WEBV, a large dataset of YouTube videos.We show results in a real-world scenario to perform open vocabulary tag annotation. Tagging of visual content is becoming more and more widespread as web-based services and social networks have popularized tagging functionalities among their users. These user-generated tags are used to ease browsing and exploration of media collections, e.g.?using tag clouds, or to retrieve multimedia content. However, not all media are equally tagged by users. Using the current systems is easy to tag a single photo, and even tagging a part of a photo, like a face, has become common in sites like Flickr and Facebook. On the other hand, tagging a video sequence is more complicated and time consuming, so that users just tag the overall content of a video. In this paper we present a method for automatic video annotation that increases the number of tags originally provided by users, and localizes them temporally, associating tags to keyframes. Our approach exploits collective knowledge embedded in user-generated tags and web sources, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr, as well as web sources like Google and Bing. Given a keyframe, our method is able to select "on the fly" from these visual sources the training exemplars that should be the most relevant for this test sample, and proceeds to transfer labels across similar images. Compared to existing video tagging approaches that require training classifiers for each tag, our system has few parameters, is easy to implement and can deal with an open vocabulary scenario. We demonstrate the approach on tag refinement and localization on DUT-WEBV, a large dataset of web videos, and show state-of-the-art results.
An early version of the proposed approach was introduced in our preliminary conference papers @cite_4 @cite_49 . In this paper we made key modifications in the algorithm and obtained significant improvements in the results. Differently from our previous work we introduce multiple types of image sources for a more effective cross-media tag transfer; we design a vote weighting procedure based on visual similarity and the use of a temporal smoothing strategy which exploits the temporal continuity of a video; further, we show a better performance in terms both of precision and recall. Finally, large-scale experiments have been carried on using a new public dataset @cite_12 @cite_53 , allowing fair comparisons w.r.t. other methods.
@cite_53 @cite_4 @cite_12 @cite_49
2070004190 2022730193 173660979 2023520647
Numerous web videos associated with rich metadata are available on the Internet today. While such metadata like video tags bring us facilitation and opportunities for video search and multimedia content understanding, some challenges also arise due to the fact that those video tags are usually annotated at the video level, while many tags actually only describe parts of the video content. How to localize the relevant parts or frames of web video for given tags is the key to many applications and research tasks. In this paper we propose combining topic model and relevance filtering to localize relevant frames. Our method is designed in three steps. First, we apply relevance filtering to assign relevance scores to video frames and a raw relevant frame set is obtained by selecting the top ranked frames. Then, we separate the frames into topics by mining the underlying semantics using latent Dirichlet allocation and use the raw relevance set as validation set to select relevant topics. Finally, the topical relevances are used to refine the raw relevant frame set and the final results are obtained. Experiment results on two real web video databases validate the effectiveness of the proposed approach. Nowadays, almost any web site that provides means for sharing user-generated multimedia content, like Flickr, Facebook, YouTube and Vimeo, has tagging functionalities to let users annotate the material that they want to share. The tags are then used to retrieve the uploaded content, and to ease browsing and exploration of these collections, e.g. using tag clouds. However, while tagging a single image is straightforward, and sites like Flickr and Facebook allow also to tag easily portions of the uploaded photos, tagging a video sequence is more cumbersome, so that users just tend to tag the overall content of a video. Moreover, the tagging process is completely manual, and often users tend to spend as few time as possible to annotate the material, resulting in a sparse annotation of the visual content. A semi-automatic process, that helps the users to tag a video sequence would improve the quality of annotations and thus the overall user experience. While research on image tagging has received a considerable attention in the latest years, there are still very few works that address the problem of automatically assigning tags to videos, locating them temporally within the video sequence. In this paper we present a system for video tag suggestion and temporal localization based on collective knowledge and visual similarity of frames. The algorithm suggests new tags that can be associated to a given keyframe exploiting the tags associated to videos and images uploaded to social sites like YouTube and Flickr and visual features. Nowadays, numerous social videos have pervaded on the Web. Social web videos are characterized with the accompanying rich contextual information which describe the content of videos and thus greatly facilitate video search and browsing. Generally those context data such as tags are generated for the whole video, without temporal indication on when they actually appear in the video. However, many tags only describe parts of the video content. Therefore, tag localization, the process of assigning tags to the underlying relevant video segments or frames is gaining increasing research interests and a benchmark dataset for the fair evaluation of tag localization algorithms is highly desirable. In this paper, we describe and release a dataset called DUT-WEBV, which contains 1550 videos collected from YouTube portal by issuing 31 concepts as queries. These concepts cover a wide range of semantic aspects including scenes like “mountain”, events like “flood”, objects like “cows”, sites like “gas station”, and activities like “handshaking”, offering great challenges to the tag (i.e., concept) localization task. For each video of a tag, we carefully annotate the time durations when the tag appears in the video. Besides the video itself, the contextual information, such as thumbnail images, titles, and categories, is also provided. Together with this benchmark dataset, we present a baseline for tag localization using multiple instance learning approach. Finally, we discuss some open research issues for tag localization in web videos. Tagging of multimedia content is becoming more and more widespread as web 2.0 sites, like Flickr and Facebook for images, YouTube and Vimeo for videos, have popularized tagging functionalities among their users. These user-generated tags are used to retrieve multimedia content, and to ease browsing and exploration of media collections, e.g. using tag clouds. However, not all media are equally tagged by users: using the current browsers is easy to tag a single photo, and even tagging a part of a photo, like a face, has become common in sites like Flickr and Facebook; on the other hand tagging a video sequence is more complicated and time consuming, so that users just tag the overall content of a video. In this paper we present a system for automatic video annotation that increases the number of tags originally provided by users, and localizes them temporally, associating tags to shots. This approach exploits collective knowledge embedded in tags and Wikipedia, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr.