William Chen

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

Motion trajectory matching of video objects

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

VideoQ: a fully automated video retrieval system using motion sketches

In this paper, we propose an efficient wavelet-based approach to achieve flexible and robust motion trajectory matching of video objects. By using the wavelet transform, our algorithm decomposes the raw object trajectory into components at different scales. We use the coarsest scale components to approximate the global motion information and the finer scale components to partition the global motion into subtrajectories. Each subtrajectory is then modeled by a set of spatial and temporal translation invariant attributes. Motion retrieval based on subtrajectory modeling has been tested and compared against other global trajectory matching schemes to show the advantages of our approach in achieving spatio-temporal invariance properties.

Generating semantic visual templates for video databases

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 spatio-temporal attributes playing a key role in video retrieval. The resulting system VideoQ, is the first on-line video search engine supporting automatic object based indexing and spatio-temporal queries.

Semantic visual templates: linking visual features to semantics

We describe a system that generates semantic visual templates (SVTs) for video databases. From a single query sketch, new queries are automatically generated with each one representing a different view of the initial sketch. The combination of the original and new queries forms a large set of potential queries for a content-based video retrieval system. Through Bayesian relevance feedback, the user narrows the choices to an exemplar set. This exemplar set, or SVTs, represents personalized views of a concept and an effective set of queries to retrieve a general category of images and videos. We have generated SVTs for several classes of videos, including sunsets, high jumpers, and slalom skiers. Our experiments show that the user can quickly converge upon SVTs with optimal performance, achieving over 85% of the precision from icons chosen by exhaustive search.