Video browsing interfaces and applications: a review

We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other

Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements

Emotion evoked by an advertisement plays a key role in influencing brand recall and eventual consumer choices. Automatic ad affect recognition has several useful applications. However, the use of content-based feature representations does not give insights into how affect is modulated by aspects such as the ad scene setting, salient object attributes and their interactions. Neither do such approaches inform us on how humans prioritize visual information for ad understanding. Our work addresses these lacunae by decomposing video content into detected objects, coarse scene structure, object statistics and actively attended objects identified via eye-gaze. We measure the importance of each of these information channels by systematically incorporating related information into ad affect prediction models. Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.Comment: Accepted for publication in the Proceedings of 20th ACM International Conference on Multimodal Interaction, Boulder, CO, US

Ambient Sound Provides Supervision for Visual Learning

The sound of crashing waves, the roar of fast-moving cars -- sound conveys important information about the objects in our surroundings. In this work, we show that ambient sounds can be used as a supervisory signal for learning visual models. To demonstrate this, we train a convolutional neural network to predict a statistical summary of the sound associated with a video frame. We show that, through this process, the network learns a representation that conveys information about objects and scenes. We evaluate this representation on several recognition tasks, finding that its performance is comparable to that of other state-of-the-art unsupervised learning methods. Finally, we show through visualizations that the network learns units that are selective to objects that are often associated with characteristic sounds.Comment: ECCV 201

Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

Evaluating Two-Stream CNN for Video Classification

Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g., "skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.Comment: ACM ICMR'1

Temporal Attention-Gated Model for Robust Sequence Classification

Typical techniques for sequence classification are designed for well-segmented sequences which have been edited to remove noisy or irrelevant parts. Therefore, such methods cannot be easily applied on noisy sequences expected in real-world applications. In this paper, we present the Temporal Attention-Gated Model (TAGM) which integrates ideas from attention models and gated recurrent networks to better deal with noisy or unsegmented sequences. Specifically, we extend the concept of attention model to measure the relevance of each observation (time step) of a sequence. We then use a novel gated recurrent network to learn the hidden representation for the final prediction. An important advantage of our approach is interpretability since the temporal attention weights provide a meaningful value for the salience of each time step in the sequence. We demonstrate the merits of our TAGM approach, both for prediction accuracy and interpretability, on three different tasks: spoken digit recognition, text-based sentiment analysis and visual event recognition.Comment: Accepted by CVPR 201