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

Automatic video annotation with forests of fuzzy decision trees

Nowadays, the annotation of videos with high-level semantic concepts or features is a great challenge. In this paper, this problem is tackled by learning, by means of Fuzzy Decision Trees (FDT), automatic rules based on a limited set of examples. Rules intended, in an exploitation step, to reduce the need of human usage in the process of indexation. However, when addressing large, unbalanced, multiclass example sets, a single classi er - such as the FDT - is insu cient. Therefore we introduce the use of forests of fuzzy decision trees (FFDT) and we highlight: (a) its e ectiveness on a high level feature detection task, compared to other competitive systems and (b) the e ect on performance from the number of classi ers point of view. Moreover, since the resulting indexes are, by their nature, to be used in a retrieval application, we discuss the results under the lights of a ranking (vs. a classi cation) context.Peer Reviewe

Who is the director of this movie? Automatic style recognition based on shot features

We show how low-level formal features, such as shot duration, meant as length of camera takes, and shot scale, i.e. the distance between the camera and the subject, are distinctive of a director's style in art movies. So far such features were thought of not having enough varieties to become distinctive of an author. However our investigation on the full filmographies of six different authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total number of 120 movies analysed second by second, confirms that these shot-related features do not appear as random patterns in movies from the same director. For feature extraction we adopt methods based on both conventional and deep learning techniques. Our findings suggest that feature sequential patterns, i.e. how features evolve in time, are at least as important as the related feature distributions. To the best of our knowledge this is the first study dealing with automatic attribution of movie authorship, which opens up interesting lines of cross-disciplinary research on the impact of style on the aesthetic and emotional effects on the viewers

Visual Concept Detection in Images and Videos

The rapidly increasing proliferation of digital images and videos leads to a situation where content-based search in multimedia databases becomes more and more important. A prerequisite for effective image and video search is to analyze and index media content automatically. Current approaches in the field of image and video retrieval focus on semantic concepts serving as an intermediate description to bridge the “semantic gap” between the data representation and the human interpretation. Due to the large complexity and variability in the appearance of visual concepts, the detection of arbitrary concepts represents a very challenging task. In this thesis, the following aspects of visual concept detection systems are addressed: First, enhanced local descriptors for mid-level feature coding are presented. Based on the observation that scale-invariant feature transform (SIFT) descriptors with different spatial extents yield large performance differences, a novel concept detection system is proposed that combines feature representations for different spatial extents using multiple kernel learning (MKL). A multi-modal video concept detection system is presented that relies on Bag-of-Words representations for visual and in particular for audio features. Furthermore, a method for the SIFT-based integration of color information, called color moment SIFT, is introduced. Comparative experimental results demonstrate the superior performance of the proposed systems on the Mediamill and on the VOC Challenge. Second, an approach is presented that systematically utilizes results of object detectors. Novel object-based features are generated based on object detection results using different pooling strategies. For videos, detection results are assembled to object sequences and a shot-based confidence score as well as further features, such as position, frame coverage or movement, are computed for each object class. These features are used as additional input for the support vector machine (SVM)-based concept classifiers. Thus, other related concepts can also profit from object-based features. Extensive experiments on the Mediamill, VOC and TRECVid Challenge show significant improvements in terms of retrieval performance not only for the object classes, but also in particular for a large number of indirectly related concepts. Moreover, it has been demonstrated that a few object-based features are beneficial for a large number of concept classes. On the VOC Challenge, the additional use of object-based features led to a superior performance for the image classification task of 63.8% mean average precision (AP). Furthermore, the generalization capabilities of concept models are investigated. It is shown that different source and target domains lead to a severe loss in concept detection performance. In these cross-domain settings, object-based features achieve a significant performance improvement. Since it is inefficient to run a large number of single-class object detectors, it is additionally demonstrated how a concurrent multi-class object detection system can be constructed to speed up the detection of many object classes in images. Third, a novel, purely web-supervised learning approach for modeling heterogeneous concept classes in images is proposed. Tags and annotations of multimedia data in the WWW are rich sources of information that can be employed for learning visual concepts. The presented approach is aimed at continuous long-term learning of appearance models and improving these models periodically. For this purpose, several components have been developed: a crawling component, a multi-modal clustering component for spam detection and subclass identification, a novel learning component, called “random savanna”, a validation component, an updating component, and a scalability manager. Only a single word describing the visual concept is required to initiate the learning process. Experimental results demonstrate the capabilities of the individual components. Finally, a generic concept detection system is applied to support interdisciplinary research efforts in the field of psychology and media science. The psychological research question addressed in the field of behavioral sciences is, whether and how playing violent content in computer games may induce aggression. Therefore, novel semantic concepts most notably “violence” are detected in computer game videos to gain insights into the interrelationship of violent game events and the brain activity of a player. Experimental results demonstrate the excellent performance of the proposed automatic concept detection approach for such interdisciplinary research

RUSHES—an annotation and retrieval engine for multimedia semantic units

Multimedia analysis and reuse of raw un-edited audio visual content known as rushes is gaining acceptance by a large number of research labs and companies. A set of research projects are considering multimedia indexing, annotation, search and retrieval in the context of European funded research, but only the FP6 project RUSHES is focusing on automatic semantic annotation, indexing and retrieval of raw and un-edited audio-visual content. Even professional content creators and providers as well as home-users are dealing with this type of content and therefore novel technologies for semantic search and retrieval are required. In this paper, we present a summary of the most relevant achievements of the RUSHES project, focusing on specific approaches for automatic annotation as well as the main features of the final RUSHES search engine

Active learning of an action detector on untrimmed videos

textCollecting and annotating videos of realistic human actions is tedious, yet critical for training action recognition systems. We propose a method to actively request the most useful video annotations among a large set of unlabeled videos. Predicting the utility of annotating unlabeled video is not trivial, since any given clip may contain multiple actions of interest, and it need not be trimmed to temporal regions of interest. To deal with this problem, we propose a detection-based active learner to train action category models. We develop a voting-based framework to localize likely intervals of interest in an unlabeled clip, and use them to estimate the total reduction in uncertainty that annotating that clip would yield. On three datasets, we show our approach can learn accurate action detectors more efficiently than alternative active learning strategies that fail to accommodate the "untrimmed" nature of real video data.Computer Science