A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm

Abstract

Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.

A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

“…To obtain the results of high extraction accuracy and reduce the number of features, an objective function was defined as an auxiliary in this paper. As the Fisher discriminant criterion has been shown to have good performance in building extraction and other extraction problems that include two categories, maximizing the differences between classes and minimizing the differences within classes, and accurately identifying the target category from other classes, it was used to define the objective function for feature selection [43]. The formula of the objective function is expressed as follows:…”

Ant Lion Optimizer for Texture Classification: A Moving Convolutional Mask

“…In order to assess the property of the proposed texture classification technique, 5 public images with obvious texture features are utilized in this section. Some commonly used swarm intelligence algorithms, for instance PSO [19], HBMO [20], GSA [21] and CS algorithms [22] are used to make a comparison, and the computational complexity is O(nlnn) for all of the algorithms above [37]. Furthermore, some other texture classification techniques such as modified GLCM [5], rotated LBP [10], directional statistical Gabor [12] and ''Tuned'' convolutional mask [18] are also used to make a further comparison.…”

“…Zheng proposed a novel approach to produce a better convolutional mask using HBMO, which was utilized in the field of texture classification, and improved the quality of the convolutional mask [20]. Wan et al introduced a texture classification by obtaining a convolutional mask using GSA, which was exemplified in a residential area and the performance is better than some commonly used techniques [21]. Peng et al utilized CS algorithm, which had better optimization ability and could produce better mask to learn a convolutional mask and extracted the water area from an image [22].…”

A Water-Area Recognition Approach Based on “Tuned” Texture Mask and Cuckoo Search Algorithm

“…As well, some existing “Tuned” mask techniques which are, respectively, proposed by Zheng and Zheng (GA [17]), Ye et al (PSO [18]), and Wan et al (GSA [19]) are used to make a comparison. The whole experiment is split into two parts: (1) Experiments on samples: obtain the optimal “Tuned” mask based on training samples and make recognition for water and nonwater testing samples.…”

“…Ye et al [18] explained the principle and steps of “Tuned” mask with the particle swarm optimization algorithm (PSO) and illustrated how to train “Tuned” mask with the proposed method in details. Wan et al [19] introduced a residential area recognition method based on “Tuned” mask and optimized with the gravitational search algorithm (GSA), which was able to keep a good balance on the efficiency and recognition accuracy. In all, GA, PSO, and GSA could obtain good “Tuned” mask, but the dimension of the optimization problem is relatively high and the value of each individual should be a real number in the range of wide continuous space, which may not guarantee the research ability in the solution space; it is worth trying more swarm intelligence algorithms on this topic.…”