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

HIERARCHICAL LEARNING OF DISCRIMINATIVE FEATURES AND CLASSIFIERS FOR LARGE-SCALE VISUAL RECOGNITION

Enabling computers to recognize objects present in images has been a long standing but tremendously challenging problem in the field of computer vision for decades. Beyond the difficulties resulting from huge appearance variations, large-scale visual recognition poses unprecedented challenges when the number of visual categories being considered becomes thousands, and the amount of images increases to millions. This dissertation contributes to addressing a number of the challenging issues in large-scale visual recognition. First, we develop an automatic image-text alignment method to collect massive amounts of labeled images from the Web for training visual concept classifiers. Specif- ically, we first crawl a large number of cross-media Web pages containing Web images and their auxiliary texts, and then segment them into a collection of image-text pairs. We then show that near-duplicate image clustering according to visual similarity can significantly reduce the uncertainty on the relatedness of Web images’ semantics to their auxiliary text terms or phrases. Finally, we empirically demonstrate that ran- dom walk over a newly proposed phrase correlation network can help to achieve more precise image-text alignment by refining the relevance scores between Web images and their auxiliary text terms. Second, we propose a visual tree model to reduce the computational complexity of a large-scale visual recognition system by hierarchically organizing and learning the classifiers for a large number of visual categories in a tree structure. Compared to previous tree models, such as the label tree, our visual tree model does not require training a huge amount of classifiers in advance which is computationally expensive. However, we experimentally show that the proposed visual tree achieves results that are comparable or even better to other tree models in terms of recognition accuracy and efficiency. Third, we present a joint dictionary learning (JDL) algorithm which exploits the inter-category visual correlations to learn more discriminative dictionaries for image content representation. Given a group of visually correlated categories, JDL simul- taneously learns one common dictionary and multiple category-specific dictionaries to explicitly separate the shared visual atoms from the category-specific ones. We accordingly develop three classification schemes to make full use of the dictionaries learned by JDL for visual content representation in the task of image categoriza- tion. Experiments on two image data sets which respectively contain 17 and 1,000 categories demonstrate the effectiveness of the proposed algorithm. In the last part of the dissertation, we develop a novel data-driven algorithm to quantitatively characterize the semantic gaps of different visual concepts for learning complexity estimation and inference model selection. The semantic gaps are estimated directly in the visual feature space since the visual feature space is the common space for concept classifier training and automatic concept detection. We show that the quantitative characterization of the semantic gaps helps to automatically select more effective inference models for classifier training, which further improves the recognition accuracy rates

combining multimodal external resources for event-based news video retrieval and question answering

Ph.DDOCTOR OF PHILOSOPH

A framework for automatic semantic video annotation

The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the ‘semantic gap’. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation