Abstract
Model-free tracking is important for solving tasks such as moving-object tracking and action recognition in cases where no prior object knowledge is available. For this purpose, we extend the concept of spatially synchronous dynamics in spin-lattice models to the spatiotemporal domain to track segments within an image sequence. The method is related to synchronization processes in neural networks and based on superparamagnetic clustering of data. Spin interactions result in the formation of clusters of correlated spins, providing an automatic labeling of corresponding image regions. The algorithm obeys detailed balance. This is an important property as it allows for consistent spin-transfer across subsequent frames, which can be used for segment tracking. Therefore, in the tracking process the correct equilibrium will always be found, which is an important advance as compared with other more heuristic tracking procedures. In the case of long image sequences, i.e., movies, the algorithm is augmented with a feedback mechanism, further stabilizing segment tracking.
Highlights
How can me make sense out of a complex visual scene having no or only little prior knowledge about its contents and the objects therein? Such problems occur, for example, if we wish to learn cause-effects in an hitherto unknown environment
We presented an algorithm for model-free segment tracking based on a novel, conjoint framework, combining local correspondences and image segmentation to synchronize the segmentation of adjacent images
The algorithm provides a partitioning of the image sequence in segments, such that points in a segment are more similar to each other than to points in another segment, and such that corresponding image points belong to the same segment
Summary
How can me make sense out of a complex visual scene having no or only little prior knowledge about its contents and the objects therein? Such problems occur, for example, if we wish to learn cause-effects in an hitherto unknown environment. Many object definitions are only meaningful within the context of a given scenario and a set of possible actions. In an unknown scenario, the tracking of image segments, presumably representing parts of objects, allows to postpone object definition to a later step of the visual-scene analysis. Several approaches for segment tracking have been proposed in the context of video segmentation [2,3,4,5,6,7,8,9]. Some approaches rely on segmenting each frame independently, e.g., by classifying pixels into regions based on similarity in the feature space, followed by a segment matching step based on their low-level features [2,3,4,5]
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