In this paper, we propose to learn temporal embeddings of video frames for complex video analysis. Large quantities of unlabeled video data can be easily obtained from the Internet. These videos possess the implicit weak label that they are sequences of temporally and semantically coherent images. We leverage this information to learn temporal embeddings for video frames by associating frames with the temporal context that they appear in. To do this, we propose a scheme for incorporating temporal context based on past and future frames in videos, and compare this to other contextual representations. In addition, we show how data augmentation using multi-resolution samples and hard negatives helps to significantly improve the quality of the learned embeddings. We evaluate various design decisions for learning temporal embeddings, and show that our embeddings can improve performance for multiple video tasks such as retrieval, classification, and temporal order recovery in unconstrained Internet video.
 Complex events consist of various human interactions with different objects in diverse environments. The evidences needed to recognize events may occur in short time periods with variable lengths and can happen anywhere in a video. This fact prevents conventional machine learning algorithms from effectively recognizing the events. In this paper, we propose a novel method that can automatically identify the key evidences in videos for detecting complex events. Both static instances (objects) and dynamic instances (actions) are considered by sampling frames and temporal segments respectively. To compare the characteristic power of heterogeneous instances, we embed static and dynamic instances into a multiple instance learning framework via instance similarity measures, and cast the problem as an Evidence Selective Ranking (ESR) process. We impose l1 norm to select key evidences while using the Infinite Push Loss Function to enforce positive videos to have higher detection scores than negative videos. The Alternating Direction Method of Multipliers (ADMM) algorithm is used to solve the optimization problem. Experiments on large-scale video datasets show that our method can improve the detection accuracy while providing the unique capability in discovering key evidences of each complex event. This paper addresses the challenge of Multimedia Event Detection by proposing a novel method for high-level and low-level features fusion based on collective classification. Generally, the method consists of three steps: training a classifier from low-level features; encoding high-level features into graphs; and diffusing the scores on the established graph to obtain the final prediction. The final prediction is derived from multiple graphs each of which corresponds to a high-level feature. The paper investigates two graph construction methods using logarithmic and exponential loss functions, respectively and two collective classification algorithms, i.e. Gibbs sampling and Markov random walk. The theoretical analysis demonstrates that the proposed method converges and is computationally scalable and the empirical analysis on TRECVID 2011 Multimedia Event Detection dataset validates its outstanding performance compared to state-of-the-art methods, with an added benefit of interpretability. Low-level appearance as well as spatio-temporal features, appropriately quantized and aggregated into Bag-of-Words (BoW) descriptors, have been shown to be effective in many detection and recognition tasks. However, their effcacy for complex event recognition in unconstrained videos have not been systematically evaluated. In this paper, we use the NIST TRECVID Multimedia Event Detection (MED11 [1]) open source dataset, containing annotated data for 15 high-level events, as the standardized test bed for evaluating the low-level features. This dataset contains a large number of user-generated video clips. We consider 7 different low-level features, both static and dynamic, using BoW descriptors within an SVM approach for event detection. We present performance results on the 15 MED11 events for each of the features as well as their combinations using a number of early and late fusion strategies and discuss their strengths and limitations. Combining multiple low-level visual features is a proven and effective strategy for a range of computer vision tasks. However, limited attention has been paid to combining such features with information from other modalities, such as audio and videotext, for large scale analysis of web videos. In our work, we rigorously analyze and combine a large set of low-level features that capture appearance, color, motion, audio and audio-visual co-occurrence patterns in videos. We also evaluate the utility of high-level (i.e., semantic) visual information obtained from detecting scene, object, and action concepts. Further, we exploit multimodal information by analyzing available spoken and videotext content using state-of-the-art automatic speech recognition (ASR) and videotext recognition systems. We combine these diverse features using a two-step strategy employing multiple kernel learning (MKL) and late score level fusion methods. Based on the TRECVID MED 2011 evaluations for detecting 10 events in a large benchmark set of ∼45000 videos, our system showed the best performance among the 19 international teams. In this paper we address the challenging problem of complex event recognition by using low-level events. In this problem, each complex event is captured by a long video in which several low-level events happen. The dataset contains several videos and due to the large number of videos and complexity of the events, the available annotation for the low-level events is very noisy which makes the detection task even more challenging. To tackle these problems we model the joint relationship between the low-level events in a graph where we consider a node for each low-level event and whenever there is a correlation between two low-level events the graph has an edge between the corresponding nodes. In addition, for decreasing the effect of weak and or irrelevant low-level event detectors we consider the presence absence of low-level events as hidden variables and learn a discriminative model by using latent SVM formulation. Using our learned model for the complex event recognition, we can also apply it for improving the detection of the low-level events in video clips which enables us to discover a conceptual description of the video. Thus our model can do complex event recognition and explain a video in terms of low-level events in a single framework. We have evaluated our proposed method over the most challenging multimedia event detection dataset. The experimental results reveals that the proposed method performs well compared to the baseline method. Further, our results of conceptual description of video shows that our model is learned quite well to handle the noisy annotation and surpass the low-level event detectors which are directly trained on the raw features.