Abstract: An approach is provided for determining and utilizing geographical locations contextually relevant to a user. A contextually relevant location platform determines location-based data associated with a user and/or user device. The contextually relevant location platform determines stationary points based, at least in part, on the location-based data. The contextually relevant location platform determines context data associated with the stationary points. The contextually relevant location platform determines at least one location anchor based, at least in part, on the stationary points and the associated context data, wherein the at least one location anchor represents a bounded geographical area of contextual relevance to the user.

Abstract: An approach is provided for determining and utilizing geographical locations contextually relevant to a user. A contextually relevant location platform determines location-based data associated with a user and/or user device. The contextually relevant location platform determines stationary points based, at least in part, on the location-based data. The contextually relevant location platform determines context data associated with the stationary points. The contextually relevant location platform determines at least one location anchor based, at least in part, on the stationary points and the associated context data, wherein the at least one location anchor represents a bounded geographical area of contextual relevance to the user.

Abstract: A method for structuring video by probabilistic merging of video segments includes the steps of obtaining a plurality of frames of unstructured video; generating video segments from the unstructured video by detecting shot boundaries based on color dissimilarity between consecutive frames; extracting a feature set by processing pairs of segments for visual dissimilarity and their temporal relationship, thereby generating an inter-segment visual dissimilarity feature and an inter-segment temporal relationship feature; and merging video segments with a merging criterion that applies a probabilistic analysis to the feature set, thereby generating a merging sequence representing the video structure. The probabilistic analysis follows a Bayesian formulation and the merging sequence is represented in a hierarchical tree structure.

Abstract: A method for structuring video by probabilistic merging of video segments includes the steps of obtaining a plurality of frames of unstructured video; generating video segments from the unstructured video by detecting shot boundaries based on color dissimilarity between consecutive frames; extracting a feature set by processing pairs of segments for visual dissimilarity and their temporal relationship, thereby generating an inter-segment visual dissimilarity feature and an inter-segment temporal relationship feature; and merging video segments with a merging criterion that applies a probabilistic analysis to the feature set, thereby generating a merging sequence representing the video structure. The probabilistic analysis follows a Bayesian formulation and the merging sequence is represented in a hierarchical tree structure.