TNO at TRECVID 2013 : multimedia event detection and instance search

We describe the TNO system and the evaluation results for TRECVID 2013 Multimedia Event Detection (MED) and instance search (INS) tasks. The MED system consists of a bag-of-word (BOW) approach with spatial tiling that uses low-level static and dynamic visual features, an audio feature and high-level concepts. Automatic speech recognition (ASR) and optical character recognition (OCR) are not used in the system. In the MED case with 100 example training videos, support-vector machines (SVM) are trained and fused to detect an event in the test set. In the case with 0 example videos, positive and negative concepts are extracted as keywords from the textual event description and events are detected with the high-level concepts. The MED results show that the SIFT keypoint descriptor is the one which contributes best to the results, fusion of multiple low-level features helps to improve the performance, and the textual event-description chain currently performs poorly. The TNO INS system presents a baseline open-source approach using standard SIFT keypoint detection and exhaustive matching. In order to speed up search times for queries a basic map-reduce scheme is presented to be used on a multi-node cluster. Our INS results show above-median results with acceptable search times.This research for the MED submission was performed in the GOOSE project, which is jointly funded by the enabling technology program Adaptive Multi Sensor Networks (AMSN) and the MIST research program of the Dutch Ministry of Defense. The INS submission was partly supported by the MIME project of the creative industries knowledge and innovation network CLICKNL.peer-reviewe

The AXES submissions at TrecVid 2013

The AXES project participated in the interactive instance search task (INS), the semantic indexing task (SIN) the multimedia event recounting task (MER), and the multimedia event detection task (MED) for TRECVid 2013. Our interactive INS focused this year on using classifiers trained at query time with positive examples collected from external search engines. Participants in our INS experiments were carried out by students and researchers at Dublin City University. Our best INS runs performed on par with the top ranked INS runs in terms of P@10 and P@30, and around the median in terms of mAP. For SIN, MED and MER, we use systems based on state- of-the-art local low-level descriptors for motion, image, and sound, as well as high-level features to capture speech and text and the visual and audio stream respectively. The low-level descriptors were aggregated by means of Fisher vectors into high- dimensional video-level signatures, the high-level features are aggregated into bag-of-word histograms. Using these features we train linear classifiers, and use early and late-fusion to combine the different features. Our MED system achieved the best score of all submitted runs in the main track, as well as in the ad-hoc track. This paper describes in detail our INS, MER, and MED systems and the results and findings of our experimen

TRECVID 2015 – An Overview of the Goals, Tasks, Data, Evaluation Mechanisms, and Metrics

International audienc

Multimodal News Summarization, Tracking and Annotation Incorporating Tensor Analysis of Memes

We demonstrate four novel multimodal methods for efficient video summarization and comprehensive cross-cultural news video understanding. First, For video quick browsing, we demonstrate a multimedia event recounting system. Based on nine people-oriented design principles, it summarizes YouTube-like videos into short visual segments (812sec) and textual words (less than 10 terms). In the 2013 Trecvid Multimedia Event Recounting competition, this system placed first in recognition time efficiency, while remaining above average in description accuracy. Secondly, we demonstrate the summarization of large amounts of online international news videos. In order to understand an international event such as Ebola virus, AirAsia Flight 8501 and Zika virus comprehensively, we present a novel and efficient constrained tensor factorization algorithm that first represents a video archive of multimedia news stories concerning a news event as a sparse tensor of order 4. The dimensions correspond to extracted visual memes, verbal tags, time periods, and cultures. The iterative algorithm approximately but accurately extracts coherent quad-clusters, each of which represents a significant summary of an important independent aspect of the news event. We give examples of quad-clusters extracted from tensors with at least 108 entries derived from international news coverage. We show the method is fast, can be tuned to give preferences to any subset of its four dimensions, and exceeds three existing methods in performance. Thirdly, noting that the co-occurrence of visual memes and tags in our summarization result is sparse, we show how to model cross-cultural visual meme influence based on normalized PageRank, which more accurately captures the rates at which visual memes are reposted in a specified time period in a specified culture. Lastly, we establish the correspondences of videos and text descriptions in different cultures by reliable visual cues, detect culture-specific tags for visual memes and then annotate videos in a cultural settings. Starting with any video with less text or no text in one culture (say, US), we select candidate annotations in the text of another culture (say, China) to annotate US video. Through analyzing the similarity of images annotated by those candidates, we can derive a set of proper tags from the viewpoints of another culture (China). We illustrate cultural-based annotation examples by segments of international news. We evaluate the generated tags by cross-cultural tag frequency, tag precision, and user studies

Scalable Machine Learning for Visual Data

Recent years have seen a rapid growth of visual data produced by social media, large-scale surveillance cameras, biometrics sensors, and mass media content providers. The unprecedented availability of visual data calls for machine learning methods that are effective and efficient for such large-scale settings. The input of any machine learning algorithm consists of data and supervision. In a large-scale setting, on the one hand, the data often comes with a large number of samples, each with high dimensionality. On the other hand, the unconstrained visual data requires a large amount of supervision to make machine learning methods effective. However, the supervised information is often limited and expensive to acquire. The above hinder the applicability of machine learning methods for large-scale visual data. In the thesis, we propose innovative approaches to scale up machine learning to address challenges arising from both the scale of the data and the limitation of the supervision. The methods are developed with a special focus on visual data, yet they are also widely applicable to other domains that require scalable machine learning methods. Learning with high-dimensionality: The "large-scale" of visual data comes not only from the number of samples but also from the dimensionality of the features. While a considerable amount of effort has been spent on making machine learning scalable for more samples, few approaches are addressing learning with high-dimensional data. In Part I, we propose an innovative solution for learning with very high-dimensional data. Specifically, we use a special structure, the circulant structure, to speed up linear projection, the most widely used operation in machine learning. The special structure dramatically improves the space complexity from quadratic to linear, and the computational complexity from quadratic to linearithmic in terms of the feature dimension. The proposed approach is successfully applied in various frameworks of large-scale visual data analysis, including binary embedding, deep neural networks, and kernel approximation. The significantly improved efficiency is achieved with minimal loss of the performance. For all the applications, we further propose to optimize the projection parameters with training data to further improve the performance. The scalability of learning algorithms is often fundamentally limited by the amount of supervision available. The massive visual data comes unstructured, with diverse distribution and high-dimensionality -- it is required to have a large amount of supervised information for the learning methods to work. Unfortunately, it is difficult, and sometimes even impossible to collect a sufficient amount of high-quality supervision, such as instance-by-instance labels, or frame-by-frame annotations of the videos. Learning from label proportions: To address the challenge, we need to design algorithms utilizing new types of supervision, often presented in weak forms, such as relatedness between classes, and label statistics over the groups. In Part II, we study a learning setting called Learning from Label Proportions (LLP), where the training data is provided in groups, and only the proportion of each class in each group is known. The task is to learn a model to predict the class labels of the individuals. Besides computer vision, this learning setting has broad applications in social science, marketing, and healthcare, where individual-level labels cannot be obtained due to privacy concerns. We provide theoretical analysis under an intuitive framework called Empirical Proportion Risk Minimization (EPRM), which learns an instance level classifier to match the given label proportions on the training data. The analysis answers the fundamental question, when and why LLP is possible. Under EPRM, we propose the proportion-SVM (∝SVM) algorithm, which jointly optimizes the latent instance labels and the classification model in a large-margin framework. The approach avoids making restrictive assumptions on the data, leading to the state-of-the-art results. We have successfully applied the developed tools to challenging problems in computer vision including instance-based event recognition, and attribute modeling. Scaling up mid-level visual attributes: Besides learning with weak supervision, the limitation on the supervision can also be alleviated by leveraging the knowledge from different, yet related tasks. Specifically, "visual attributes" have been extensively studied in computer vision. The idea is that the attributes, which can be understood as models trained to recognize visual properties can be leveraged in recognizing novel categories (being able to recognize green and orange is helpful for recognizing apple). In a large-scale setting, the unconstrained visual data requires a high-dimensional attribute space that is sufficiently expressive for the visual world. Ironically, though designed to improve the scalability of visual recognition, conventional attribute modeling requires expensive human efforts for labeling the detailed attributes and is inadequate for designing and learning a large set of attributes. To address such challenges, in Part III, we propose methods that can be used to automatically design a large set of attribute models, without user labeling burdens. We propose weak attribute, which combines various types of existing recognition models to form an expressive space for visual recognition and retrieval. In addition, we develop category-level attribute to characterize distinct properties separating multiple categories. The attributes are optimized to be discriminative to the visual recognition task over known categories, providing both better efficiency and higher recognition rate over novel categories with a limited number of training samples