Consumer Video Understanding: A Benchmark Database and An Evaluation of Human and Machine Performance

Recognizing visual content in unconstrained videos has become a very important problem for many applications. Existingcorpora for video analysis lack scale and/or content diversity,and thus limited the needed progress in this critical area. In this paper, we describe and release a new database called CCV, containing 9,317 web videos over 20 semantic categories, including events like “baseball” and “parade”, scenes like “beach”, and objects like “cat”. The database was collected with extra care to ensure relevance to consumer interest and originality of video content without post-editing. Such videos typically have very little textual annotation and thus can benefit from the development of automatic content analysis techniques. We used Amazon MTurk platform to perform manual annotation, and studied the behaviors and performance of human annotators on MTurk.We also compared the abilities in understanding consumer video content by humans and machines. For the latter, we implemented automatic classifiers using state-of-the-art multi-modal approach that achieved top performance in recent TRECVID multimedia event detection task. Results confirmed classifiers fusing audio and video features significantly outperform single-modality solutions. We also found that humans are much better at understanding categories of nonrigid objects such as “cat”, while current automatic techniques are relatively close to humans in recognizing categories that have distinctive background scenes or audio patterns

ViTS: Video tagging system from massive web multimedia collections

The popularization of multimedia content on the Web has arised the need to automatically understand, index and retrieve it. In this paper we present ViTS, an automatic Video Tagging System which learns from videos, their web context and comments shared on social networks. ViTS analyses massive multimedia collections by Internet crawling, and maintains a knowledge base that updates in real time with no need of human supervision. As a result, each video is indexed with a rich set of labels and linked with other related contents. ViTS is an industrial product under exploitation with a vocabulary of over 2.5M concepts, capable of indexing more than 150k videos per month. We compare the quality and completeness of our tags with respect to the ones in the YouTube-8M dataset, and we show how ViTS enhances the semantic annotation of the videos with a larger number of labels (10.04 tags/video), with an accuracy of 80,87%.Postprint (published version

A Data-Driven Approach for Tag Refinement and Localization in Web Videos

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.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU

The VIA Annotation Software for Images, Audio and Video

In this paper, we introduce a simple and standalone manual annotation tool for images, audio and video: the VGG Image Annotator (VIA). This is a light weight, standalone and offline software package that does not require any installation or setup and runs solely in a web browser. The VIA software allows human annotators to define and describe spatial regions in images or video frames, and temporal segments in audio or video. These manual annotations can be exported to plain text data formats such as JSON and CSV and therefore are amenable to further processing by other software tools. VIA also supports collaborative annotation of a large dataset by a group of human annotators. The BSD open source license of this software allows it to be used in any academic project or commercial application.Comment: to appear in Proceedings of the 27th ACM International Conference on Multimedia (MM '19), October 21-25, 2019, Nice, France. ACM, New York, NY, USA, 4 page

Leveraging video annotations in video-based e-learning

The e-learning community has been producing and using video content for a long time, and in the last years, the advent of MOOCs greatly relied on video recordings of teacher courses. Video annotations are information pieces that can be anchored in the temporality of the video so as to sustain various processes ranging from active reading to rich media editing. In this position paper we study how video annotations can be used in an e-learning context - especially MOOCs - from the triple point of view of pedagogical processes, current technical platforms functionalities, and current challenges. Our analysis is that there is still plenty of room for leveraging video annotations in MOOCs beyond simple active reading, namely live annotation, performance annotation and annotation for assignment; and that new developments are needed to accompany this evolution.Comment: 7th International Conference on Computer Supported Education (CSEDU), Barcelone : Spain (2014

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

A framework for automatic semantic video annotation

The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the ‘semantic gap’. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation

Automatic tagging and geotagging in video collections and communities

Automatically generated tags and geotags hold great promise to improve access to video collections and online communi- ties. We overview three tasks offered in the MediaEval 2010 benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features