We describe a system for synchronization and organization of user-contributed content from live music events. We start with a set of short video clips taken at a single event by multiple contributors, who were using a varied set of capture devices. Using audio fingerprints, we synchronize these clips such that overlapping clips can be displayed simultaneously. Furthermore, we use the timing and link structure generated by the synchronization algorithm to improve the findability and representation of the event content, including identifying key moments of interest and descriptive text for important captured segments of the show. We also identify the preferred audio track when multiple clips overlap. We thus create a much improved representation of the event that builds on the automatic content match. Our work demonstrates important principles in the use of content analysis techniques for social media content on the Web, and applies those principles in the domain of live music capture.

We propose a system for automatically detecting the ways in which images have been copied and edited or manipulated. We draw upon these manipulation cues to construct probable parent-child relationships between pairs of images, where the child image was derived through a series of visual manipulations on the parent image. Through the detection of these relationships across a plurality of images, we can construct a history of the image, called the visual migration map (VMM), which traces the manipulations applied to the image through past generations. We propose to apply VMMs as part of a larger internet image archaeology system (IIAS), which can process a given set of related images and surface many interesting instances of images from within the set. In particular, the image closest to the "original" photograph might be among the images with the most descendants in the VMM. Or, the images that are most deeply descended from the original may exhibit unique differences and changes in the perspective being conveyed by the author. We evaluate the system across a set of photographs crawled from the web and find that many types of image manipulations can be automatically detected and used to construct plausible VMMs. These maps can then be successfully mined to find interesting instances of images and to suppress uninteresting or redundant ones, leading to a better understanding of how images are used over different times, sources, and contexts.

Can we leverage the community-contributed collections of rich media on the web to automatically generate representative and diverse views of the world’s landmarks? We use a combination of context- and content-based tools to generate representative sets of images for location-driven features and landmarks, a common search task. To do that, we using location and other metadata, as well as tags associated with images, and the images ’ visual features. We present an approach to extracting tags that represent landmarks. We show how to use unsupervised methods to extract representative views and images for each landmark. This approach can potentially scale to provide better search and representation for landmarks, worldwide. We evaluate the system in the context of image search using a real-life dataset of 110,000 images from the San Francisco area.

We conduct a broad survey of query-adaptive search strategies in a variety of application domains, where the internal retrieval mechanisms used for search are adapted in response to the anticipated needs for each individual query experienced by the system. While these query-adaptive approaches can range from meta-search over text collections to multimodal search over video databases, we propose that all such systems can be framed and discussed in the context of a single, unified framework. In our paper, we keep an eye towards the domain of video search, where search cues are available from a rich set of modalities, including textual speech transcripts, low-level visual features, and high-level semantic concept detectors. The relative efficacy of each of the modalities is highly variant between many types of queries. We observe that the state of the art in query-adaptive retrieval frameworks for video collections is highly dependent upon the definition of classes of queries, which are groups of queries that share similar optimal search strategies, while many applications in text and Web retrieval have included many advanced strategies, such as direct prediction of search method performance and inclusion of contextual cues from the searcher. We conclude that such advanced strategies previously developed for text retrieval have a broad range of possible applications in future research in multimodal video search.

Multimedia search over distributed sources often result in recurrent images or videos which are manifested beyond the textual modality. To exploit such contextual patterns and keep the simplicity of the keyword-based search, we propose novel reranking methods to leverage the recurrent patterns to improve the initial text search results. The approach, context reranking, is formulated as a random walk problem along the context graph, where video stories are nodes and the edges between them are weighted by multimodal contextual similarities. The random walk is biased with the preference towards stories with higher initial text search scores - a principled way to consider both initial text search results and their implicit contextual relationships. When evaluated on TRECVID 2005 video benchmark, the proposed approach can improve retrieval on the average up to 32% relative to the baseline text search method in terms of story-level Mean Average Precision. In the people-related queries, which usually have recurrent coverage across news sources, we can have up to 40% relative improvement. Most of all, the proposed method does not require any additional input from users (e.g., example images), or complex search models for special queries (e.g., named person search).

The advent of media-sharing sites like Flickr and YouTube has drastically increased the volume of community-contributed multimedia resources available on the web. These collections have a previously unimagined depth and breadth, and have generated new opportunities - and new challenges - to multimedia research. How do we analyze, understand and extract patterns from these new collections? How can we use these unstructured, unrestricted community contributions of media (and annotation) to generate "knowledge".

We propose a novel and generic video/image reranking algorithm, IB reranking, which reorders results from text-only searches by discovering the salient visual patterns of relevant and irrelevant shots from the approximate relevance provided by text results. The IB reranking method, based on a rigorous Information Bottleneck (IB) principle, finds the optimal clustering of images that preserves the maximal mutual information between the search relevance and the high-dimensional low-level visual features of the images in the text search results. Evaluating the approach on the TRECVID 2003-2005 data sets shows significant improvement upon the text search baseline, with relative increases in average performance of up to 23%. The method requires no image search examples from the user, but is competitive with other state-of-the-art example-based approaches. The method is also highly generic and performs comparably with sophisticated models which are highly tuned for specific classes of queries, such as named-persons. Our experimental analysis has also confirmed the proposed reranking method works well when there exist sufficient recurrent visual patterns in the search results, as often the case in multi-source news videos.

We propose a layered dynamic mixture model for asynchronous multi-modal fusion for unsupervised pattern discovery in video. The lower layer of the model uses generative temporal structures such as a hierarchical hidden Markov model to convert the audiovisual streams into mid-level labels, it also models the correlations in text with probabilistic latent semantic analysis. The upper layer fuses the statistical evidence across diverse modalities with a flexible meta-mixture model that assumes loose temporal correspondence. Evaluation on a large news database shows that multi-modal clusters have better correspondence to news topics than audio-visual clusters alone; novel analysis techniques suggest that meaningful clusters occur when the prediction of salient features by the model concurs with those shown in the story clusters.

We develop a framework for the automatic discovery of query classes for query-class-dependent search models in multimodal retrieval. The framework automatically discovers useful query classes by clustering queries in a training set according to the performance of various unimodal search methods, yielding classes of queries which have similar fusion strategies for the combination of unimodal components for multimodal search. We further combine these performance features with the semantic features of the queries during clustering in order to make discovered classes meaningful. The inclusion of the semantic space also makes it possible to choose the correct class for new, unseen queries, which have unknown performance space features. We evaluate the system against the TRECVID 2004 automatic video search task and find that the automatically discovered query classes give an improvement of 18% in MAP over hand-defined query classes used in previous works. We also find that some hand-defined query classes, such as "Named Person" and "Sports" do, indeed, have similarities in search method performance and are useful for query-class-dependent multimodal search, while other hand-defined classes, such as "Named Object" and "General Object" do not have consistent search method performance and should be split apart or replaced with other classes. The proposed framework is general and can be applied to any new domain without expert domain knowledge.

We present our new results in news video story segmentation and classification in the context of the TRECVID video retrieval benchmarking event 2003. We applied and extended the maximum entropy statistical model to fuse diverse features effectively from multiple levels and modalities, including visual, audio, and text. We have included various features such as motion, face, music/speech types, prosody, and high-level text segmentation information. The statistical fusion model is used to discover automatically relevant features contributing to the detection of story boundaries. One novel aspect of our method is the use of a feature wrapper to address different types of features - asynchronous, discrete, continuous and delta ones. We also developed several novel features related to prosody. Using the large news video set from the TRECVID 2003 benchmark, we demonstrate satisfactory performance (F1 measure up to 0.76) and, more importantly, observe an interesting opportunity for further improvement.

The work presents the first effort to automatically annotate the semantic meanings of temporal video patterns obtained through unsupervised discovery processes. This problem is interesting in domains where neither perceptual patterns nor semantic concepts have simple structures. The patterns in video are modeled with hierarchical hidden Markov models (HHMM), with efficient algorithms to learn the parameters, the model complexity and the relevant features; the meanings are contained in words of the speech transcript of the video. The pattern-word association is obtained via cooccurrence analysis and statistical machine translation models. Promising results are obtained through extensive experiments on 20+ hours of TRECVID news videos: video patterns that associate with distinct topics such as el-nino and politics are identified; the HHMM temporal structure model compares favorably to a nontemporal clustering algorithm.