Visual precis generation using coresets

Given an image stream, our on-line algorithm will select the semantically-important images that summarize the visual experience of a mobile robot. Our approach consists of data pre-clustering using coresets followed by a graph based incremental clustering procedure using a topic based image representation. A coreset for an image stream is a set of representative images that semantically compresses the data corpus, in the sense that every frame has a similar representative image in the coreset. We prove that our algorithm efficiently computes the smallest possible coreset under natural well-defined similarity metric and up to provably small approximation factor. The output visual summary is computed via a hierarchical tree of coresets for different parts of the image stream. This allows multi-resolution summarization (or a video summary of specified duration) in the batch setting and a memory-efficient incremental summary for the streaming case.

Authors

• Rohan Paul
• Dan Feldman
• Daniela Rus
• Paul Newman 0001

Keywords

• Streaming media
• Clustering algorithms
• Visualization
• Approximation algorithms
• Approximation methods
• Robots
• Image coding
• Mobile Robot
• Online Algorithm
• Visual Summary
• Image Stream
• Running Time
• Entire Dataset
• Points In Space
• Number Of Centers
• Topic Modeling
• Closest Point
• Gibbs Sampling
• Central Idea
• Unit Sphere
• Perceptual Similarity
• Topic Distribution
• Binary Tree
• Representative Subject
• Central Set
• Binary Search
• Iterative Point
• Subject Space
• Facility Location Problem