Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2012.08.053
Title: Locally connected graph for visual tracking
Authors: Lu, K.
Ding, Z.
Ge, S. 
Keywords: Feature extraction
Graph embedding
Subspace learning
Visual tracking
Issue Date: 23-Nov-2013
Source: Lu, K., Ding, Z., Ge, S. (2013-11-23). Locally connected graph for visual tracking. Neurocomputing 120 : 45-53. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2012.08.053
Abstract: Based on fast feature extraction, the subspace representation model provides a compact notion of the "thing" being tracked rather than treating the target as a sparse feature representation. The main challenges of the subspace representation model can be attributed to the difficulty of handling the appearance variability of a target object. In this paper, we present a subspace learning algorithm based on graph embedding that uses a Locally Connected Graph (LCG). By constructing a supervised graph with several types of labeled target samples, the algorithm can effectively learn the semantic subspace modeling for some appearance variability. Moreover, by using an additional constraint connection among several subgraphs, the algorithm can obtain a more compact subspace model. In comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the recent literature. © 2013 Elsevier B.V.
Source Title: Neurocomputing
URI: http://scholarbank.nus.edu.sg/handle/10635/56515
ISSN: 09252312
DOI: 10.1016/j.neucom.2012.08.053
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