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首页> 美国政府科技报告 >SOFTCBIR: Object Searching in Videos Combining Keypoint Matching and Graduated Assignment.
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SOFTCBIR: Object Searching in Videos Combining Keypoint Matching and Graduated Assignment.

机译：sOFTCBIR：结合关键点匹配和分级分配的视频中的对象搜索。

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This paper proposes a new approach to object searching in video databases, SoftCBIR, which combines a keypoint matching algorithm and a graduated assignment algorithm based on 'softassign'. Compared with previous approaches, SoftCBIR is an innovative combination of two powerful techniques: (1) An energy minimization algorithm is applied to match two groups of keypoints while accounting for both their similarity in descriptor space and the consistency of their geometric configuration. The algorithm computes correspondence and pose transformation between two groups of keypoints iteratively and alternately toward an optimal result. The objective energy function combines normalized distance errors in descriptor space and in the spatial domain. (2) Initial individual keypoint matching relies on Approximate K-Nearest Neighbor (ANN) search. ANN achieves much more accurate initial keypoint matching results in the descriptor space than K-means labeling. Experiments prove the effectiveness of our approach, and demonstrate the performance improvements rising from the combination of the two proposed techniques in the SoftCBIR algorithm.

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