SOFTCBIR: Object Searching in Videos Combining Keypoint Matching and Graduated Assignment
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2006
- Accession Number
- ADA448477
Entities
People
- Daniel Dementhon
- David S. Doermann
- Ming Luo
- Xiaodong Yu
Organizations
- University of Maryland