Improved Fast Generation of Superpixel Algorithms with Deep Network

Superpixels are the result of over-segmentation of the image and provide an intermediate representation of the image data. It plays an important role in the research of computer vision and other fields. However, the existing superpixel algorithms are non-differentiable and the deep networks are usually defined over regular grid. Thus, most of the current superpixel generation algorithms are based on hand-crafted pixel features. To this end, this paper proposes an improved fast generation of superpixel algorithms with deep network. Firstly, the deep network with multiple hidden layers is directly embedded in the process of superpixel generation to extract the pixel features of the image. Secondly, this paper calculates the initial seed point position by [K-means] clustering method to signi-ficantly improve the segmentation result, and an active search method is used to ensure the correctness of the pixel label. Finally, superpixel segmentation results are obtained. On the Berkeley dataset BSDS500, the benchmark eva-luation using BSDS is compared with other literatures, and the experimental results show that the proposed algo-rithm performs relatively well in terms of the compactness and regularity of segmentation results.

Abstract licence: CC BY-NC

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