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Conference Paper
Accelerating Local Feature Extraction using Two Stage Feature Selection and Partial Gradient Computation
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- Authors
- Keundong Lee, Seungjae Lee, Weon-Geun Oh
- Issue Date
- 2014-11
- Citation
- Asian Conference on Computer Vision (ACCV) 2014 : Workshops (LNCS 9010), v.9010, pp.366-380
- Language
- English
- Type
- Conference Paper
- Project Code
- 14MS3400, Development of The Smart Mobile Search Technology based on UVD(Unified Visual Descriptor), Oh Weon Geun
- Abstract
- In this paper, we present a fast local feature extraction method, which is our contribution to ongoing MPEG standardization of compact descriptor for visual search (CDVS). To reduce time complexity of feature extraction, two-stage feature selection, which is based on the feature selection method of CDVS Test Model (TM), and partial gradient computation are introduced. The proposed method is examined on SIFT and compared to SIFT and SURF extractor with the previous feature selection method. In addition, the proposed method is compared to various feature extraction methods of the current CDVS TM 11 in CDVS evaluation framework. Experimental results show that the proposed method significantly reduces the time complexity while maintaining the matching and retrieval performance of previous work. For its efficiency, the proposed method has been integrated into CDVS TM since 107th MPEG meeting. This method will be also useful for feature extraction on mobile devices, where the use of computational resource is limited.
- KSP Keywords
- Evaluation Framework, Feature selection(FS), Gradient computation, Its efficiency, Local feature extraction, Mobile devices, Retrieval performance, SIFT and SURF, Time Complexity, Two-Stage, Visual search