We present a higher-level visual representation, visual synset, for object categorization. The visual synset improves the traditional bag of words representation with better discrimination and invariance power. First, the approach strengthens the inter-class discrimination power by constructing an intermediate visual descriptor, delta visual phrase, from frequently co-occurring visual word-set with similar spatial context. Second, the approach achieves better intra-class invariance power, by clustering delta visual phrases into visual synset, based their probabilistic dasiasemanticspsila, i.e. class probability distribution. Hence, the resulting visual synset can partially bridge the visual differences of images of same class. The tests on Caltech-101 and Pascal-VOC 05 dataset demonstrated that the proposed image representation can achieve good accuracies.

Personalization especially in the domain of information retrieval is essentially important, as users might pose the same query even when they are searching for different information. It is thus necessary to create a retrieval engine which takes into consideration the dynamic information needs of different users. This paper presents our personalized news video retrieval engine, which exploits the individual userpsilas previous browsing history to customize and enhance their future search results. Specifically, the system utilizes the news topic hierarchy, a hierarchical news topic structure derived from unsupervised clustering on the news video corpus and event entities from news video and online news articles. We then dynamically project userpsilas browsing history onto this topic hierarchy to provide the basis for re-ranking relevant news videos. This system is tested on one month of TRECVID 2006 dataset consisting of 80 hours news video and found to return results in a more intuitive and personalized manner.

In this paper, we highlight the use of multimedia technology in generating intrinsic summaries of tourism related information. The system utilizes an automated process to gather, filter and classify information on various tourist spots on the Web. The end result present to the user is a personalized multimedia summary generated with respect to users queries filled with text, image, video and real-time news made retrievable for mobile devices. Preliminary experiments demonstrate the superiority of our presentation scheme to traditional methods.

The massive amount of multimedia information especially video available on the Web requires a more precise and interactive retrieval. Current operational video retrieval systems do not make use of the implicit visual features but rely only on textual metadata supplied by the user during uploading. This greatly affects the retrieval performance as the metadata may not be comprehensive or consistent. In this paper, we describe the use of a spatio-temporal visual map (STVM) model to supplement Web video retrieval. This is done by employing the spatio-temporal visual similarity to rerank the text-retrieval results and find new results. Experimental results on a dynamic Web video corpus show significant improvement based on STVM model, with good usability scores based on human users.

Current state-of-the-art news video retrieval systems mainly focus on automated speech recognition (ASR) text to perform retrieval. This paradigm greatly affects retrieval performance as ASR text alone is not sufficient to provide an accurate representation of the entire news video. In this paper, we describe our automated retrieval framework which fuses the multimodal features and event structures present in news video to support precise news video retrieval. The contributions of this paper are: (a) we uncover and employ temporal event clusters to provide additional information during story level retrieval; and (b) we integrate other modality features with text features and incorporate event clusters for pseudo relevance feedback (PRF) in shot level re-ranking. Experiments performed on video search task using the TRECVID 2005/06 dataset show that the proposed approach is effective.

Existing video research incorporates the use of relevance feedback based on user-dependent interpretations to improve the retrieval results. In this paper, we segregate the process of relevance feedback into 2 distinct facets: (a) recall-directed feedback; and (b) precision-directed feedback. The recall-directed facet employs general features such as text and high level features (HLFs) to maximize efficiency and recall during feedback, making it very suitable for large corpuses. The precision-directed facet on the other hand uses many other multimodal features in an active learning environment for improved accuracy. Combined with a performance-based adaptive sampling strategy, this process continuously re-ranks a subset of instances as the user annotates. Experiments done using TRECVID 2006 dataset show that our approach is efficient and effective.

Earlier research in news video has been focusing mainly on improving retrieval accuracies given the limited amount of extractable video semantics. In this paper, we propose an enhancement to news video searching by leveraging extractable video semantics coupled with relevant external information resources to support event-based analysis; leading to discovery of topic hierarchy for browsing key events and supporting question answering (QA). We introduce topic browsing based on news structures obtained through hierarchical clustering and threading, with emphasis on interesting events determined by measuring the amount of "web activities" on these events on Blog sites. For QA, we employ extensive query analysis to obtain various query features in addition to the topic hierarchical structures to answer both context-oriented and visual-oriented questions. Our main contributions includes: (a) combining multimodal event information extracted from news video, web news articles and news blogs to support event analysis, (b) introducing topic evolution browsing based on users' interest and (c) extending QA on top of topic hierarchy to handle various types of specialized video queries. Experiments performed on 70 hours of multilingual news from TRECVID 2005 dataset shows that the proposed approach is effective and appealing to users.

Precise automated video search is gaining in importance as the amount of multimedia information is increasing at exponential rates. One of the drawbacks that make video retrieval difficult is the lack of available semantics. In this paper, we propose to supplement the semantic knowledge for retrieval by providing useful semantic clusters derived from event entities present in the news video. These entities include the output from keywords derived from the automated speech recognition (ASR) and event-related High-level Features (HLF) extracted from the news video at the pseudo story level. Fuzzy clustering is then carried out to group similar stories together to form semantic clusters. The retrieval system utilizes these clusters to refine the re-ranking process in the Pseudo Relevance Feedback (PRF) step. Initial experiments performed on video search task using the TRECVID 2005 dataset show that the proposed approach can improve the search performance significantly.

Person identification is very important in the domain of multimedia news as it is often the focus of events in news stories and interest of searchers. However, this detection is impeded by the imprecise audio/visual analysis tools. In this paper, we describe a multimodal and multi-faceted approach to person-x detection in news video. We make use of multimodal features extracted from text, visual and audio inherent in news video. We also incorporate multiple external sources of news from Web and parallel news archives to extract location and temporal profile of the persons. We call this second source of information the multi-faceted context. The multimodal, multi-faceted information is then fused using a rank boosting approach. Experiments on TRECVID 2003 and 2004 search queries demonstrate that our approach is effective

When querying a news video archive, the users are interested in retrieving precise answers in the form of a summary that best answers the query. However, current video retrieval systems, including the search engines on the web, are designed to retrieve documents instead of precise answers. This research explores the use of question answering (QA) techniques to support personalized news video retrieval. Users interact with our system, VideoQA, using short natural language questions with implicit constraints on contents, context, duration, and genre of expected videos. VideoQA returns short precise news video summaries as answers. The main contributions of this research are: (a) the extension of QA technology to support QA in news video; and (b) the use of multi-modal features, including visual, audio, textual, and external resources, to help correct speech recognition errors and to perform precise question answering. The system has been tested on 7 days of news video and has been found to be effective.