Automatic Segmentation of Product Bottle Label Based on GrabCut Algorithm
International Journal of Contents
In this paper, we propose a method to build an accurate initial trimap for the GrabCut algorithm without the need for human interaction. First, we identify a rough candidate for the label region of a bottle by applying a saliency map to find a salient area from the image. Then, the Hough Transformation method is used to detect the left and right borders of the label region, and the k-means algorithm is used to localize the upper and lower borders of the label of the bottle. These four borders
... e used to build an initial trimap for the GrabCut method. Finally, GrabCut segments accurate regions for the label. The experimental results for 130 wine bottle images demonstrated that the saliency map extracted a rough label region with an accuracy of 97.69% while also removing the complex background. The Hough transform and projection method accurately drew the outline of the label from the saliency area, and then the outline was used to build an initial trimap for GrabCut. Finally, the GrabCut algorithm successfully segmented the bottle label with an average accuracy of 92.31%. Therefore, we believe that our method is suitable for product label recognition systems that automatically segment product labels. Although our method achieved encouraging results, it has some limitations in that unreliable results are produced under conditions with varying illumination and reflections. Therefore, we are in the process of developing preprocessing algorithms to improve the proposed method to take into account variations in illumination and reflections. information on the camera based smart phone application. For example, wine label recognition can provide the win type and how to drink the win. We pay the attention on the product bottle as our experimental object. Prior to access the required information, the most of important step is the object segmentation that is to separate ROI(Region Of Interest) object of interest from the input images which is captured by a high-definition smart phone, and then following steps can be go on such as image analysis, recognition, retrieval stage and so on. This paper is organized as follows. Section 2 represents some state-of-art object segmentation approaches (GrabCut algorithm). The preprocessing of system is described in section 3, and we present the procedure of initial trimap building for object segmentation, following by the graph-based method, GrabCut method, is used to separate the product label region of interest from the complicated background. The experimental results are shown in section 4 and at last we discuss the conclusion in section 5. Related Work Image segmentation is the first step of image processing and the basic of computer vision. It is a key step in image processing and analysis. It not only reduces the volume of data but also extract the image structure features for advanced processing steps, image analysis and recognition. Due to the segmentation errors are propagated to the high-level processing http://dx.