Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Incremental spectral clustering by efficiently updating the eigen-system
Pattern Recognition
A novel approach for salient image regions detection and description
Pattern Recognition Letters
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A graph model for mutual information based clustering
Journal of Intelligent Information Systems
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words
IEEE Transactions on Robotics
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
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Keyframe detection is a fundamental component in approaches for large-scale mapping and scene recognition. Assuming that the detection is applied to a set of continuously captured frames, this paper presents a keyframe detector that not only considers the frame content to quantify appearance changes on the sequence, but also the temporal accumulation of evidence. If frames are described as a set of local features, our algorithm proposes a unified framework for comparing local features acquired from consecutive frames by the building of an auxiliary graph-based on the locality of features. Spectral clustering is then employed to obtain tentative graph partitions. Validated partitions will be associated to keyframes. It should be noted that the approach does not need to estimate the motion of the camera, and that the similarity measure defined within this framework can be used for any sort of feature. Experimental results using different types of visual features show the strength of our representation. Moreover, an evaluation methodology has been defined for the quantitative comparison of our keyframe detector against other similar approaches.