Model-based graph-cut method for automatic flower segmentation with spatial constraints Ezzeddine Zagrouba ⁎, Siwar Ben Gamra, Asma Najjar Team of research SIIVA — Lab. Riadi, Higher Institute of Computer Science, University of Tunis Elmanar, Tunisia abstract article info Article history: Received 22 March 2013 Received in revised form 9 August 2014 Accepted 11 August 2014 Available online 30 September 2014 Keywords: Automatic flower image segmentation Graph-cut Spatial prior In this paper, we present an accelerated system for segmenting flower images based on graph-cut technique which formulates the segmentation problem as an energy function minimization. The contribution of this paper consists to propose an improvement of the classical used energy function, which is composed of a data- consistent term and a boundary term. For this, we integrate an additional data-consistent term based on the spa- tial prior and we add gradient information in the boundary term. Then, we propose an automated coarse-to-fine segmentation method composed mainly of two levels: coarse segmentation and fine segmentation. First, the coarse segmentation level is based on minimizing the proposed energy function. Then, the fine segmentation is done by optimizing the energy function through the standard graph-cut technique. Experiments were per- formed on a subset of Oxford flower database and the obtained results are compared to the reimplemented method of Nilsback et al. [1]. The evaluation shows that our method consumes less CPU time and it has a satisfac- tory accuracy compared with the mentioned method above [1]. © 2014 Elsevier B.V. All rights reserved. 1. Introduction Automatic flower classification systems are important for a wide range of application including pharmacy research, environment protec- tion and perfume production. Thanks to computer vision, image pro- cessing and pattern recognition techniques, automatic recognition systems make the identification of the flower category easier by analyz- ing color images. Image segmentation is generally considered an impor- tant component of the recognition or classification processes, and affects the quality of the image analysis. Automatic flower segmentation allows the extraction of the object of interest (foreground) from the rest of the image (background) without any manual interaction. The majority of flower images present natural scenes with complex background. The areas surrounding the flowers have generally large va- riety of colors and textures. It can contain several entities distributed separately or together such as stones, leaves, turf grass, green foliage and soil. Fig. 1 illustrates different types of elements that can be contained in the area surrounding the flower. As the flowers from differ- ent species may look very similar both in shape and color, the use of the background information to generate the image features can increase this similarity and consequently reduce the classification accuracy. Therefore, we believe that the extraction of features only from the object of interest provides more meaningful and accurate information than the one obtained from the whole image. Although many flower image segmentation methods have been proposed in the literature, it remains difficult to find a general solution that is applicable to all types of flowers and gives accurate results. In the next paragraph, we present the state of the art on flower image segmentation. Das et al. [2] proposed an iterative segmentation algorithm using color and spatial domain knowledge-driven feedback. Their method mapped the RGB color space to commonly used color names in order to delete pixels which belong to background color classes like black, brown, green or gray. The foreground region represented by the remaining colors is accepted if it is included in the flower area. In order to define the flower region, some hypotheses were made such as the flower centroid should fall within the “central region” of the image. Saitoh et al. [3] presented the Normalized Cost (NC) method to extract flower regions. It is based on a Dynamic Programming method known as the intelligent scissors [4] for extracting the boundary of the object of interest. The image is rep- resented as a directed weighted graph where nodes are pixels and arcs between neighboring pixels represent the 8-connectivity information. This method consists in computing the local minimum cost given by a path between two seeds. The obtained cost is normalized by the length of this path. The shortest path in the graph gives the object edges. In this work, the authors assume that the flower is at the center of the image and the background occupies the peripheral area. Based on this hy- pothesis, the authors determine some local minimum points of each local cost profile along the straight line from the starting point to all the middle points of four sides. Then, they extract the boundary for each local mini- mum point based on the NC and they select the one that has the smallest normalized cost and contains the center point. Another interesting auto- matic algorithm can be found in [1]. The first step of this algorithm aims Image and Vision Computing 32 (2014) 1007–1020 ⁎ Corresponding author at: Higher Institute of Computer Science (ISI), 2 rue Abourraihan Al Bayrouni, 2080 Ariana, Tunisia. Fax: +216 71 706 698. E-mail address: ezzeddine.zagrouba@fsm.rnu.tn (E. Zagrouba). http://dx.doi.org/10.1016/j.imavis.2014.08.012 0262-8856/© 2014 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Image and Vision Computing journal homepage: www.elsevier.com/locate/imavis