Video scene detection is the task of temporally segmenting a video into its basic story units called scenes. We propose a temporal context aware scene detection method. For each shot in a video, we store the time-indexed features of its surrounding shots as its context memory. A context-reading operation is performed to read the most relevant information from the memory which is used to update the feature of the query shot. To adaptively determine the temporal scale of context memory for different queries, we apply a bank of context memories of different temporal scales to generate multiple context reads, and adaptively aggregate them according to their confidence scores. The adaptive context-reading is guided by a structure learning objective which encourages each shot to read the most appropriate context such that the global structure of scene can be revealed in the feature space. With the context-aware shot features learned by our method, we perform clustering to find the scene boundaries. Our experiments demonstrate that adaptively modeling temporal context yields the state-of-the-art results on the existing video scene detection datasets. We also construct a large-scale dataset for the task and our ablation studies on it show that the performance gains owe to the proposed adaptive context reading.

Automatic image aesthetics assessment is important for a wide variety of applications such as on-line photo suggestion, photo album management and image retrieval. Previous methods have focused on mapping the holistic image content to a high or low aesthetics rating. However, the composition information of an image characterizes the harmony of its visual elements according to the principles of art, and provides richer information for learning aesthetics. In this work, we propose to model the image composition information as the mutual dependency of its local regions, and design a novel architecture to leverage such information to boost the performance of aesthetics assessment. To achieve this, we densely partition an image into local regions and compute aesthetics-preserving features over the regions to characterize the aesthetics properties of image content. With the feature representation of local regions, we build a region composition graph in which each node denotes one region and any two nodes are connected by an edge weighted by the similarity of the region features. We perform reasoning on this graph via graph convolution, in which the activation of each node is determined by its highly correlated neighbors. Our method naturally uncovers the mutual dependency of local regions in the network training procedure, and achieves the state-of-the-art performance on the benchmark visual aesthetics datasets.

We investigate automatic inference of uploader intent for online video, i.e., prediction of the reason for which a user has uploaded a particular video to the Internet. Users upload video for specific reasons, but rarely state these reasons explicitly in the video metadata. Information about the reasons motivating uploaders has the potential ultimately to benefit a wide range of application areas, including video production, video-based advertising , and video search. In this paper, we apply a combination of social-Web mining and crowdsourcing to arrive at a typology that characterizes the uploader intent of a broad range of videos. We then use a set of multimodal features, including visual semantic features, found to be indicative of uploader intent in order to classify videos automatically into uploader intent classes. We evaluate our approach on a dataset containing ca. 3K crowdsourcing-annotated videos and demonstrate its usefulness in prediction tasks relevant to common application areas.

While approaches based on bags of features excel at low-level action classification, they are ill-suited for recognizing complex events in video, where concept-based temporal representations currently dominate. This paper proposes a novel representation that captures the temporal dynamics of windowed mid-level concept detectors in order to improve complex event recognition. We first express each video as an ordered vector time series, where each time step consists of the vector formed from the concatenated confidences of the pre-trained concept detectors. We hypothesize that the dynamics of time series for different instances from the same event class, as captured by simple linear dynamical system (LDS) models, are likely to be similar even if the instances differ in terms of low-level visual features. We propose a two-part representation composed of fusing: (1) a singular value decomposition of block Hankel matrices (SSID-S) and (2) a harmonic signature (HS) computed from the corresponding eigen-dynamics matrix. The proposed method offers several benefits over alternate approaches: our approach is straightforward to implement, directly employs existing concept detectors and can be plugged into linear classification frameworks. Results on standard datasets such as NIST's TRECVID Multimedia Event Detection task demonstrate the improved accuracy of the proposed method.

This paper addresses the fundamental question -- How do humans recognize complex events in videos? Normally, humans view videos in a sequential manner. We hypothesize that humans can make high-level inference such as an event is present or not in a video, by looking at a very small number of frames not necessarily in a linear order. We attempt to verify this cognitive capability of humans and to discover the Minimally Needed Evidence (MNE) for each event. To this end, we introduce an online game based event quiz facilitating selection of minimal evidence required by humans to judge the presence or absence of a complex event in an open source video. Each video is divided into a set of temporally coherent microshots (1.5 secs in length) which are revealed only on player request. The player's task is to identify the positive and negative occurrences of the given target event with minimal number of requests to reveal evidence. Incentives are given to players for correct identification with the minimal number of requests.

Animated GIFs are everywhere on the Web. Our work focuses on the computational prediction of emotions perceived by viewers after they are shown animated GIF images. We evaluate our results on a dataset of over 3,800 animated GIFs gathered from MIT's GIFGIF platform, each with scores for 17 discrete emotions aggregated from over 2.5M user annotations - the first computational evaluation of its kind for content-based prediction on animated GIFs to our knowledge. In addition, we advocate a conceptual paradigm in emotion prediction that shows delineating distinct types of emotion is important and is useful to be concrete about the emotion target. One of our objectives is to systematically compare different types of content features for emotion prediction, including low-level, aesthetics, semantic and face features. We also formulate a multi-task regression problem to evaluate whether viewer perceived emotion prediction can benefit from jointly learning across emotion classes compared to disjoint, independent learning.

In this paper, we propose a discriminative representation of a video shot based on its camera motion and demonstrate how the representation can be used for high level multimedia tasks like complex event recognition. In our technique, we assume that a homography exists between a pair of subsequent frames in a given shot. Using purely image-based methods, we compute homography parameters that serve as coarse indicators of the ambient camera motion. Next, using Lie algebra, we map the homography matrices to an intermediate vector space that preserves the intrinsic geometric structure of the transformation. The mappings are stacked temporally to generate vector time-series per shot. To extract meaningful features from time-series, we propose an efficient linear dynamical system based technique. The extracted temporal features are further used to train linear SVMs as classifiers for a particular shot class. In addition to demonstrating the efficacy of our method on a novel dataset, we extend its applicability to recognize complex events in large scale videos under unconstrained scenarios. Our empirical evaluations on eight cinematographic shot classes show that our technique performs close to approaches that involve extraction of 3-D trajectories using computationally prohibitive structure from motion techniques.

Recognition of complex events in unconstrained Internet videos is a challenging research problem. In this symposium proposal, we present a systematic decomposition of complex events into hierarchical components and make an in-depth analysis of how existing research are being used to cater to various levels of this hierarchy. We also identify three key stages where we make novel contributions which are necessary to not only improve the overall recognition performance, but also develop richer understanding of these events. At the lowest level, our contributions include (a) compact covariance descriptors of appearance and motion features used in sparse coding framework to recognize realistic actions and gestures, and (b) a Lie-algebra based representation of dominant camera motion present in video shots which can be used as a complementary feature for video analysis. In the next level, we propose an (c) efficient maximum likelihood estimate based representation from low-level features computed from videos which demonstrates state of the art performance in large scale visual concept detection, and finally, we propose to (d) model temporal interactions between concepts detected in video shots through two new discriminative feature spaces derived from Linear dynamical systems which eventually boosts event recognition performance. In all cases, we conduct thorough experiments to demonstrate promising performance gains over some of the prominent approaches.

In this paper we propose a novel aesthetic model emphasizing psycho-visual statistics extracted from multiple levels in contrast to earlier approaches that rely only on descriptors suited for image recognition or based on photographic principles. At the lowest level, we determine dark-channel, sharpness and eye-sensitivity statistics over rectangular cells within a frame. At the next level, we extract Sentibank features (1,200 pre-trained visual classifiers) on a given frame, that invoke specific sentiments such as "colorful clouds", "smiling face" etc. and collect the classifier responses as frame-level statistics. At the topmost level, we extract trajectories from video shots. Using viewer's fixation priors, the trajectories are labeled as foreground, and background/camera on which statistics are computed. Additionally, spatio-temporal local binary patterns are computed that capture texture variations in a given shot. Classifiers are trained on individual feature representations independently. On thorough evaluation of 9 different types of features, we select the best features from each level -- dark channel, affect and camera motion statistics. Next, corresponding classifier scores are integrated in a sophisticated low-rank fusion framework to improve the final prediction scores. Our approach demonstrates strong correlation with human prediction on 1,000 broadcast quality videos released by NHK as an aesthetic evaluation dataset.

In this paper, we present an efficient alternative to the traditional vocabulary based on bag-of-visual words (BoW) used for visual classification tasks. Our representation is both conceptually and computationally superior to the bag-of-visual words: (1) We iteratively generate a Maximum Likelihood estimate of an image given a set of characteristic features in contrast to the BoW methods where an image is represented as a histogram of visual words, (2) We randomly sample a set of characteristic features instead of employing computation-intensive clustering algorithms used during the vocabulary generation step of BoW methods. Our performance compares favorably to the state-of-the-art on experiments over three challenging human action and a scene categorization dataset, demonstrating the universal applicability of our method.

This article presents an interactive application that enables users to improve the visual aesthetics of their digital photographs using several novel spatial recompositing techniques. This work differs from earlier efforts in two important aspects: (1) it focuses on both photo quality assessment and improvement in an integrated fashion, (2) it enables the user to make informed decisions about improving the composition of a photograph. The tool facilitates interactive selection of one or more than one foreground objects present in a given composition, and the system presents recommendations for where it can be relocated in a manner that optimizes a learned aesthetic metric while obeying semantic constraints. For photographic compositions that lack a distinct foreground object, the tool provides the user with crop or expansion recommendations that improve the aesthetic appeal by equalizing the distribution of visual weights between semantically different regions. The recomposition techniques presented in the article emphasize learning support vector regression models that capture visual aesthetics from user data and seek to optimize this metric iteratively to increase the image appeal. The tool demonstrates promising aesthetic assessment and enhancement results on variety of images and provides insightful directions towards future research.

We present an interactive application that enables users to improve the visual aesthetics of their digital photographs using spatial recomposition. Unlike earlier work that focuses either on photo quality assessment or interactive tools for photo editing, we enable the user to make informed decisions about improving the composition of a photograph and to implement them in a single framework. Specifically, the user interactively selects a foreground object and the system presents recommendations for where it can be moved in a manner that optimizes a learned aesthetic metric while obeying semantic constraints. For photographic compositions that lack a distinct foreground object, our tool provides the user with cropping or expanding recommendations that improve its aesthetic quality. We learn a support vector regression model for capturing image aesthetics from user data and seek to optimize this metric during recomposition. Rather than prescribing a fully-automated solution, we allow user-guided object segmentation and inpainting to ensure that the final photograph matches the user's criteria. Our approach achieves 86% accuracy in predicting the attractiveness of unrated images, when compared to their respective human rankings. Additionally, 73% of the images recomposited using our tool are ranked more attractive than their original counterparts by human raters.

The technique of using virtual machines for grid jobs as trusted execution environments has been hampered by the overheads involved creating the virtual machines. This paper attempts to reduce the overheads of dynamically creating and destroying the virtual environments for secure job execution. It broaches a grid architecture which we call Nova, consisting of extremely minuscule, pre-created virtual machines whose configurations could be altered with respect to the application executed within it. The benefits of the architecture are supported by experimental claims.