Hari Sundaram

A fully automated content-based video search engine supporting spatiotemporal queries

The rapidity with which digital information, particularly video, is being generated has necessitated the development of tools for efficient search of these media. Content-based visual queries have been primarily focused on still image retrieval. In this paper, we propose a novel, interactive system on the Web, based on the visual paradigm, with spatiotemporal attributes playing a key role in video retrieval. We have developed innovative algorithms for automated video object segmentation and tracking, and use real-time video editing techniques while responding to user queries. The resulting system, called VideoQ , is the first on-line video search engine supporting automatic object-based indexing and spatiotemporal queries. The system performs well, with the user being able to retrieve complex video clips such as those of skiers and baseball players with ease.

VideoQ: an automated content based video search system using visual cues

The rapidity with which digitat information, particularly video, is being generated, has necessitated the development of tools for efficient search of these media. Content based visual queries have been primarily focussed on still image retrieval. In this papel; we propose a novel, real-time, interactive system on the Web, based on the visual paradigm, with spatio-temporal attributesplaying a key role in video retrieval. We have developed algorithms for automated video object segmentation and tracking and use real-time video editing techniques while responding to user queries. The resulting system pe

MetaFac: community discovery via relational hypergraph factorization

orms well, with the user being able to retrieve complex video clips such as those of skiers, baseball players, with ease.

Determining computable scenes in films and their structures using audio-visual memory models

This paper aims at discovering community structure in rich media social networks, through analysis of time-varying, multi-relational data. Community structure represents the latent social context of user actions. It has important applications in information tasks such as search and recommendation. Social media has several unique challenges. (a) In social media, the context of user actions is constantly changing and co-evolving; hence the social context contains time-evolving multi-dimensional relations. (b) The social context is determined by the available system features and is unique in each social media website. In this paper we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from various social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multi-relational and multi-dimensional social data; (2) an efficient factorization method for community extraction on a given metagraph; (3) an on-line method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world social data collected from the Digg social media website suggest that our technique is scalable and is able to extract meaningful communities based on the social media contexts. We illustrate the usefulness of our framework through prediction tasks. We outperform baseline methods (including aspect model and tensor analysis) by an order of magnitude.

A utility framework for the automatic generation of audio-visual skims

In this paper we present novel algorithms for computing scenes and within-scene structures in films. We begin by mapping insights from film-making rules and experimental results from the psychology of audition into a computational scene model. We define a computable scene to be a chunk of audio-visual data that exhibits long-term consistency with regard to three properties: (a) chromaticity (b) lighting (c) ambient sound. Central to the computational model is the notion of a causal, finite-memory viewer model. We segment the audio and video data separately. In each case we determine the degree of correlation of the most recent data in the memory with the past. The respective scene boundaries are determined using local minima and aligned using a nearest neighbor algorithm. We introduce a periodic analysis transform to automatically determine the structure within a scene. We then use statistical tests on the transform to determine the presence of a dialogue. The algorithms were tested on a difficult data set: five commercial films. We take the first hour of data from each of the five films. The best results: scene detection: 88% recall and 72% precision, dialogue detection: 91% recall and 100% precision.

Video scene segmentation using video and audio features

In this paper, we present a novel algorithm for generating audio-visual skims from computable scenes. Skims are useful for browsing digital libraries, and for on-demand summaries in set-top boxes. A computable scene is a chunk of data that exhibits consistencies with respect to chromaticity, lighting and sound. There are three key aspects to our approach: (a) visual complexity and grammar, (b) robust audio segmentation and (c) an utility model for skim generation. We define a measure of visual complexity of a shot, and map complexity to the minimum time for comprehending the shot. Then, we analyze the underlying visual grammar, since it makes the shot sequence meaningful. We segment the audio data into four classes, and then detect significant phrases in the speech segments. The utility functions are defined in terms of complexity and duration of the segment. The target skim is created using a general constrained utility maximization procedure that maximizes the information content and the coherence of the resulting skim. The objective function is constrained due to multimedia synchronization constraints, visual syntax and by penalty functions on audio and video segments. The user study results indicate that the optimal skims show statistically significant differences with other skims with compression rates up to 90%.

Computable scenes and structures in films

We present a novel algorithm for video scene segmentation. We model a scene as a semantically consistent chunk of audio-visual data. Central to the segmentation framework is the idea of a finite-memory model. We separately segment the audio and video data into scenes, using data in the memory. The audio segmentation algorithm determines the correlations amongst the envelopes of audio features. The video segmentation algorithm determines the correlations amongst shot key-frames. The scene boundaries in both cases are determined using local correlation minima. Then, we fuse the resulting segments using a nearest neighbor algorithm that is further refined using a time-alignment distribution derived from the ground truth. The algorithm was tested on a difficult data set; the first hour of a commercial film with good results. It achieves a scene segmentation accuracy of 84%.

Blog Community Discovery and Evolution Based on Mutual Awareness Expansion

We present a computational scene model and also derive novel algorithms for computing audio and visual scenes and within-scene structures in films. We use constraints derived from film-making rules and from experimental results in the psychology of audition, in our computational scene model. Central to the computational model is the notion of a causal, finite-memory viewer model. We segment the audio and video data separately. In each case, we determine the degree of correlation of the most recent data in the memory with the past. The audio and video scene boundaries are determined using local maxima and minima, respectively. We derive four types of computable scenes that arise due to different kinds of audio and video scene boundary synchronizations. We show how to exploit the local topology of an image sequence in conjunction with statistical tests, to determine dialogs. We also derive a simple algorithm to detect silences in audio. An important feature of our work is to introduce semantic constraints based on structure and silence in our computational model. This results in computable scenes that are more consistent with human observations. The algorithms were tested on a difficult data set: three commercial films. We take the first hour of data from each of the three films. The best results: computational scene detection: 94%; dialogue detection: 91%; and recall 100% precision.

Event Mining in Multimedia Streams

There are information needs involving costly decisions that cannot be efficiently satisfied through conventional Web search engines. Alternately, community centric search can provide multiple viewpoints to facilitate decision making. We propose to discover and model the temporal dynamics of thematic communities based on mutual awareness, where the awareness arises due to observable blogger actions and the expansion of mutual awareness leads to community formation. Given a query, we construct a directed action graph that is time-dependent, and weighted with respect to the query. We model the process of mutual awareness expansion using a random walk process and extract communities based on the model. We propose an interaction space based representation to quantify community dynamics. Each community is represented as a vector in the interaction space and its evolution is determined by a novel interaction correlation method. We have conducted experiments with a real-world blog dataset and have promising results for detection as well as insightful results for community evolution.

What makes conversations interesting?: themes, participants and consequences of conversations in online social media

Events are real-world occurrences that unfold over space and time. Event mining from multimedia streams improves the access and reuse of large media collections, and it has been an active area of research with notable progress. This paper contains a survey on the problems and solutions in event mining, approached from three aspects: event description, event-modeling components, and current event mining systems. We present a general characterization of multimedia events, motivated by the maxim of five ldquoWrdquos and one ldquoHrdquo for reporting real-world events in journalism: when, where, who, what, why, and how. We discuss the causes for semantic variability in real-world descriptions, including multilevel event semantics, implicit semantics facets, and the influence of context. We discuss five main aspects of an event detection system. These aspects are: the variants of tasks and event definitions that constrain system design, the media capture setup that collectively define the available data and necessary domain assumptions, the feature extraction step that converts the captured data into perceptually significant numeric or symbolic forms, statistical models that map the feature representations to richer semantic descriptions, and applications that use event metadata to help in different information-seeking tasks. We review current event-mining systems in detail, grouping them by the problem formulations and approaches. The review includes detection of events and actions in one or more continuous sequences, events in edited video streams, unsupervised event discovery, events in a collection of media objects, and a discussion on ongoing benchmark activities. These problems span a wide range of multimedia domains such as surveillance, meetings, broadcast news, sports, documentary, and films, as well as personal and online media collections. We conclude this survey with a brief outlook on open research directions.