Feature selection is a key factor for the performance of machine learning algorithms, as not all data and hence features are related to the various tasks. In this paper, we propose a novel scheme for convolutional feature selection for machine learning algorithms in computer vision. As not all the convolutional features are related to visual tracking, removing the unrelated ones will dramatically reduce the complexity and improve the algorithm performance. However, how to identify and select features related to the visual tracking task is still a challenge for machine learning algorithms. In the proposed scheme, a novel adaptive weights-objective function approach is established to evaluate and select the features. Furthermore, a quadratic programming method is introduced which improves the optimization efficiency. The experimental results demonstrate that our proposed scheme achieves superior performance compared to the state-of-art trackers on the challenging benchmarks in computer vision.
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