Abstract
An image semantic segmentation algorithm using fully convolutional network (FCN) integrated with the recently proposed simple linear iterative clustering (SLIC) that is based on boundary term (BSLIC) is developed. To improve the segmentation accuracy, the developed algorithm combines the FCN semantic segmentation results with the superpixel information acquired by BSLIC. During the combination process, the superpixel semantic annotation is newly introduced and realized by the four criteria. The four criteria are used to annotate a superpixel region, according to FCN semantic segmentation result. The developed algorithm can not only accurately identify the semantic information of the target in the image, but also achieve a high accuracy in the positioning of small edges. The effectiveness of our algorithm is evaluated on the dataset PASCAL VOC 2012. Experimental results show that the developed algorithm improved the target segmentation accuracy in comparison with the traditional FCN model. With the BSLIC superpixel information that is involved, the proposed algorithm can get 3.86%, 1.41%, and 1.28% improvement in pixel accuracy (PA) over FCN-32s, FCN-16s, and FCN-8s, respectively.
Highlights
Research on image semantic segmentation has been well-developed over decades [1,2,3,4,5,6,7,8,9]
In order to facilitate the expression of image semantics, PASCAL VOC 2012 assigns specific color labels and numbers to each category so that different categories of targets can be distinguished by color or numbers in image semantic segmentation
Superpixel edge information is combined with the original fully convolutional network (FCN) model, where superpixel semantic annotation is applied to the combination process
Summary
Research on image semantic segmentation has been well-developed over decades [1,2,3,4,5,6,7,8,9]. The full-supervised semantic segmentation is developed by probabilistic graphical models (PGM), such as the generative model [1] and the discriminative model [10]. These models are based on the assumption of conditional independence, which might be too restrictive for many applications. Unlike the traditional recognition [12,13,14,15,16,17] and segmentation [18,19] methods, FCN can be regarded as a CNN variant, which has the ability to extract the features of objects in the image. Despite the power and flexibility of the FCN model, it still has some problems
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