AMOS - Semi-automatic Semantic Object Segmentation & Search

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AMOS is a video object segmentation and retrieval system. In this framework, a video object (e.g. person, car) is modeled and tracked as a set of regions with corresponding visual features and spatio-temporal relations. The region-based model also provides an effective base for similarity retrieval of video objects.

AMOS effectively combines user input and automatic region segmentation for defining and tracking video objects at a semantic level. First, the user roughly outlines the contour of an object at the starting frame, which is used to create a video object with underlying homogeneous regions. This process is based on a region segmentation method that involves color and edge features and a region aggregation method that classifies regions into foreground and background. Then, the object and the homogeneous regions are tracked through successive frames. This process uses affine motion models to project regions from frame to frame and a color-based region growing to determine the final projected regions. Users can stop the segmentation at any time to correct the contour of video objects. Extensive experimental results have demonstrated excellent results. Most tracking errors are caused by uncovered regions and can be corrected with a few user inputs.

AMOS also extracts salient regions within video objects that users can interactively create and manipulate. Visual features and spatio-temporal relations are computed for video objects and salient regions and stored in a database for similarity matching. The features include motion trajectory, dominant color, texture, shape, and time descriptors. Currently three types of relations among the regions of a video object are supported: orientation spatial (angle between two regions), topological spatial (contains, does not contain, or inside), and directional temporal (start before, at the same time, or after). Users can enter textual annotations for the objects. AMOS accepts queries in the form of sketches or examples and returns similar video objects based on different features and relations. The query process of finding candidate video objects for a query uses a filtering together with a joining scheme. The first step is to find a list candidate regions from the database for each query region based on the visual features. Then, the region lists are joined to obtain candidate objects and their total distance to the query is computed by matching the spatio-temporal relations.










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Last updated: June 12, 2002.