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Extracting object skeleton and its scale features in natural images is helpful for object detection and recognition in computer vision. In order to advance the location accuracy of object skeleton pixels, a new method via multi-task and variable coefficient loss function is proposed in this paper. Adopting the hierarchical integration mechanism to mutually refine captured features at different network layers; a specific variable coefficient loss function is designed for multi-class imbalanced data handling problem, such as the skeleton pixels in natural images are always far less than the non-skeleton pixels; the regression algorithm is an added deep learning branch in the skeleton extraction network assisting the improvement of recognition accuracy. Besides, not only the skeleton pixels and its classification can be obtained, but also its scales are predicted without disturbing skeleton acquisition process. The experimental results verify that both the skeleton accuracy and the generalization abilities are promoted benefiting from the regression task and the new loss function in the new method, as satisfactory results are achieved on three public datasets, i.e., SK-LARGE, SK-SMALL, and WH-SYMMAX, which are indicated by F-measures and precision/recall curves. The results further demonstrate that the proposed method is superior to the best skeleton extraction method available currently.

An Improved Skeleton Extraction Method via Multi-Task and Variable Coefficient Loss Function in Natural Images