Web video is often used as a source of data in various fields of study. While specialized subsets of web video, mainly earmarked for dedicated purposes, are often analyzed in detail, there is little information available about the properties of web video as a whole. In this paper we present insights gained from the analysis of the metadata associated with more than 120 million videos harvested from two popular web video platforms, vimeo and YouTube, in 2016 and compare their properties with the ones found in commonly used video collections. This comparison has revealed that existing collections do not (or no longer) properly reflect the properties of web video "in the wild".
Web video gained importance in various areas within the sciences and humanities in recent years. In computer science, it is mostly used in the context of retrieval @cite_9 and machine learning @cite_21 @cite_7 . To facilitate research in these areas, multiple video collections have been compiled over the years. The majority of these datasets was created with a specific problem in mind and they are therefore comprised of a comparatively small number of videos with a narrow range of content. Such collections are usually used for the evaluation of concept recognition tasks @cite_20 or action recognition tasks @cite_8 . Other, more general collections range in size from a few tens @cite_16 @cite_0 to many thousands of hours of video content @cite_10 @cite_14 @cite_4 . The more popular of these include CC @cite_6 , MCG-WEBV @cite_3 and IACC @cite_10 . What these collections have in common is that they all source their videos from the web, but all of them have their own criteria for inclusion of videos. This introduces biases which prevents these collections from being representatives for the state of web video as a whole.
@cite_14 @cite_4 @cite_7 @cite_8 @cite_9 @cite_21 @cite_6 @cite_3 @cite_0 @cite_16 @cite_10 @cite_20
2014464472 2073546118 2297970248 2007510844
User-generated contents play an important role in the Internet video-sharing activities. Techniques for summarizing the user-generated videos (UGVs) into short representative clips are useful in many applications. This paper introduces an approach for UGV summarization based on semantic recognition. Different from other types of videos like movies or broadcasting news, where the semantic contents may vary greatly across different shots, most UGVs have only a single long shot with relatively consistent high-level semantics. Therefore, a few semantically representative segments are generally sufficient for a UGV summary, which can be selected based on the distribution of semantic recognition scores. In addition, due to the poor shooting quality of many UGVs, factors such as camera shaking and lighting condition are also considered to achieve more pleasant summaries. Experiments on over 100 UGVs with both subjective and objective evaluations show that our approach clearly outperforms several alternative methods and is highly efficient. Using a regular laptop, it can produce a summary for a 2-minute video in just 10 seconds. This paper begins by considering a number of important design questions for a large-scale, widely available, multimedia test collection intended to support long-term scientific evaluation and comparison of content-based video analysis and exploitation systems. While the collection presented here is not quite web-scale, it is to our knowledge the largest video collection created to date. It is therefore of use in expanding the scale of any evaluation of multimedia collections and systems. Such exploitation systems would include the kinds of functionality already explored within the annual TREC Video Retrieval Evaluation (TRECVid) benchmarking activity such as search, semantic concept detection, and automatic summarization. We then report on our progress in creating such a multimedia collection from publicly available Internet Archive videos with Creative Commons licenses (IACC.1), which we hope will be a useful approximation of a web-scale collection and will support a next generation of benchmarking activities for content-based video operations. We also report on some possibilities for putting this collection to use in multimedia system evaluation. It is the intended that this collection be partitioned and used within the TRECVid 2010 evaluations, and in subsequent years to that. Now a days, the Data Engineering becoming emerging trend to discover knowledge from web audio- visual data such as- YouTube videos, Yahoo Screen, Face Book videos etc. Different categories of web video are being shared on such social websites and are being used by the billions of users all over the world. The uploaded web videos will have different kind of metadata as attribute information of the video data. The metadata attributes defines the contents and features characteristics of the web videos conceptually. Hence, accomplishing web video mining by extracting features of web videos in terms of metadata is a challenging task. In this work, effective attempts are made to classify and predict the metadata features of web videos such as length of the web videos, number of comments of the web videos, ratings information and view counts of the web videos using data mining algorithms such as Decision tree J48 and navie Bayesian algorithms as a part of web video mining. The results of Decision tree J48 and navie Bayesian classification models are analyzed and compared as a step in the process of knowledge discovery from web videos. This paper proposes a new video representation for few-example event recognition and translation. Different from existing representations, which rely on either low-level features, or pre-specified attributes, we propose to learn an embedding from videos and their descriptions. In our embedding, which we call VideoStory, correlated term labels are combined if their combination improves the video classifier prediction. Our proposed algorithm prevents the combination of correlated terms which are visually dissimilar by optimizing a joint-objective balancing descriptiveness and predictability. The algorithm learns from textual descriptions of video content, which we obtain for free from the web by a simple spidering procedure. We use our VideoStory representation for few-example recognition of events on more than 65K challenging web videos from the NIST TRECVID event detection task and the Columbia Consumer Video collection. Our experiments establish that i) VideoStory outperforms an embedding without joint-objective and alternatives without any embedding, ii) The varying quality of input video descriptions from the web is compensated by harvesting more data, iii) VideoStory sets a new state-of-the-art for few-example event recognition, outperforming very recent attribute and low-level motion encodings. What is more, VideoStory translates a previously unseen video to its most likely description from visual content only.