期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 24, 期 10, 页码 2955-2970出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2428052
关键词
Slow feature analysis; online kernel learning; change detection; temporal segmentation; tracking
资金
- Engineering and Physical Sciences Research Council (EPSRC) [EP/J017787/1, EP/L026813/1]
- Adaptive Facial Deformable Models for Tracking
- European Community [645094]
- Intel
- Engineering and Physical Sciences Research Council [EP/H016988/1, EP/J017787/1, EP/L026813/1] Funding Source: researchfish
- EPSRC [EP/H016988/1, EP/L026813/1, EP/J017787/1] Funding Source: UKRI
Slow feature analysis (SFA) is a dimensionality reduction technique which has been linked to how visual brain cells work. In recent years, the SFA was adopted for computer vision tasks. In this paper, we propose an exact kernel SFA (KSFA) framework for positive definite and indefinite kernels in Krein space. We then formulate an online KSFA which employs a reduced set expansion. Finally, by utilizing a special kind of kernel family, we formulate exact online KSFA for which no reduced set is required. We apply the proposed system to develop a SFA-based change detection algorithm for stream data. This framework is employed for temporal video segmentation and tracking. We test our setup on synthetic and real data streams. When combined with an online learning tracking system, the proposed change detection approach improves upon tracking setups that do not utilize change detection.
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