# A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC
## 1 Introduction  
Research on image semantic segmentation has been well-developed over decades [1; 2; 3; 4; 5; 6; 7; 8; 9]. Image semantic segmentation is a process in which an image is divided into several non-overlapping meaningful regions with semantic annotations being labeled. In an early study, 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. To solve this problem, the condition random field model (CRF) [11] is proposed, which allows for us to get the correlation between variables. Moreover, the CRF model has the ability to exploit the local texture features and the contextual information. However, the CRF model can't acquire the overall shape feature of the image, which might cause misunderstanding during the analysis of a single target. Currently, one of the most popular deep learning techniques for semantic segmentation is the fully convolutional network (FCN) [4]. 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. By replacing the fully connected layers with the convolutional layers, the classical CNN model, such as AlexNet [20], VGGNet [21], and GoogLeNet [22], is transformed into the FCN model. When compared with the CNN-based methods, the obtained model can achieve a significant improvement in segmentation accuracy. Despite the power and flexibility of the FCN model, it still has some problemsthat hinder its application to certain situations: small objects are often ignored and misclassified as background, the detailed structures of an object are often lost or smoothed. In 2003, [PERSON] et al. [23] proposed a segmentation method, named as superpixel. A superpixel can be treated as the set of pixels that are similar in location, color, texture, etc. It reduces the number of entities to be labeled semantically and enable feature computation on bigger, more meaningful regions. There are several methods to generate superpixel, such as Turbopixels [24], SuperPB [25], SEEDS [26], and SLIC [27]. By comparison, SLIC is significantly more efficient and accurate than others. In the previous work, BSLIC is presented [28; 29] for better superpixel generation that is based on SLIC. BSLIC can generate superpixels with a better trade-off between the regularity of shape and the fitting degree of the edge, so as to keep the detailed structures of an object. In this paper, we propose a semantic segmentation algorithm by combining FCN with BSLIC. FCN is typically good at extracting the overall shape of an object. However, FCN ignores to focus on the detailed structures of an object, which still has room to improve. Meanwhile, BSLIC is used to efficiently align the SLIC superpixels to image edges and to obtain better segmentation results. In contrast to SLIC, BSLIC adopts a simpler edge detecting algorithm to obtain the complete boundary information. To combine FCN semantic segmentation result and BSLIC superpixel information, we newly introduce the superpixel semantic annotation, which is realized by four criteria. With the four criteria, the tsuperpixel region is annotated by FCN semantic segmentation result. By this means, the detailed structures of an object can be kept to some extent. The proposed algorithm can not only extract the high-level abstract features of the image, but also take full advantages of the detail of the image to improve the segmentation accuracy of the model. The rest of the paper is organized as follows: In Section 2, the theory that is related to the proposed algorithm is introduced, including the fully convolution network and the BSLIC superpixel segmentation. The details of the algorithm in this paper are described in Section 3, and the experimental results are given in Section 4.