This paper presents a novel retrieval pipeline for video collections, which aims to retrieve the most significant parts of an edited video for a given query, and represent them with thumbnails which are at the same time semantically meaningful and aesthetically remarkable. Videos are first segmented into coherent and story-telling scenes, then a retrieval algorithm based on deep learning is proposed to retrieve the most significant scenes for a textual query. A ranking strategy based on deep features is finally used to tackle the problem of visualizing the best thumbnail. Qualitative and quantitative experiments are conducted on a collection of edited videos to demonstrate the effectiveness of our approach.
The process of producing thumbnails to represent video content has been widely studied. Most conventional methods for video thumbnail selection have focused on learning visual representativeness purely from visual content @cite_13 @cite_19 ; however, more recent researches have focused on choosing query-dependent thumbnails to supply specific thumbnails for different queries. Craggs al @cite_22 introduced the concept that thumbnails are surrogates for videos, as they take the place of a video in search results. Therefore, they may not accurately represent the content of the video, and create an , i.e. a discrepancy between the information sought by the user and the actual content of the video. To reduce the intention gap, they propose a new kind of animated preview, constructed of frames taken from a full video, and a crowdsourced tagging process which enables the matching between query terms and videos. Their system, while going in the right direction, suffers from the need of manual annotations, which are often expensive and difficult to obtain.
@cite_19 @cite_13 @cite_22
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The power of video over still images is the ability to represent dynamic activities. But video browsing and retrieval are inconvenient due to inherent spatio-temporal redundancies, where some time intervals may have no activity, or have activities that occur in a small image region. Video synopsis aims to provide a compact video representation, while preserving the essential activities of the original video. We present dynamic video synopsis, where most of the activity in the video is condensed by simultaneously showing several actions, even when they originally occurred at different times. For example, we can create a "stroboscopic movie", where multiple dynamic instances of a moving object are played simultaneously. This is an extension of the still stroboscopic picture. Previous approaches for video abstraction addressed mostly the temporal redundancy by selecting representative key-frames or time intervals. In dynamic video synopsis the activity is shifted into a significantly shorter period, in which the activity is much denser. Video examples can be found online in http: www.vision.huji.ac.il synopsis With the rapid explosion of video data, compact representation of videos is becoming more and more desirable for efficient browsing and communication, which leads to a number of research works on video summarization in recent years. Among these works, summaries based on a set of still frames are frequently studied and applied due to its high compactness. However, the representativeness of the selected frames, which are taken as the compact representation of the video or video segment, has not been well studied. It is observed that frame representativeness is highly related to the following elements: image quality, user attention measure, visual details, and displaying duration. It is also observed that users have similar tendency in selecting the most representative frame for a certain video segment. In this paper, we developed a method to examine and evaluate the representativeness of video frames based on learning users' perceptive evaluations. During online search, the user's expectations often differ from those of the author. This is known as the "intention gap" and is particularly problematic when searching for and discriminating between online video content. An author uses description and meta-data tags to label their content, but often cannot predict alternate interpretations or appropriations of their work. To address this intention gap, we present ThumbReels, a concept for query-sensitive video previews generated from crowdsourced, temporally defined semantic tagging. Further, we supply an open-source tool that supports on-the-fly temporal tagging of videos, whose output can be used for later search queries. A first user study validates the tool and concept. We then present a second study that shows participants found ThumbReels to better represent search terms than contemporary preview techniques.