## 1 Introduction  
The image descriptor provides information about the image by extracting / determining features such as shape or color. Image descriptors have two different types: dense / sparse. Dense descriptors are the approaches that extract features from the image in a pixel-by-pixel. One of the most common dense descriptors is the Local Binary Pattern (LBP). Scale Invariant Feature Transform (SIFT) and Speeded up Robust Features (SURF) are examples of instances in which sparse descriptors evaluate each pixel in the image and extract the models [1]. Automatic programming is the process which the machine generates program code automatically [2]. GP, the most well-known automatic programming method, was developed by [PERSON] [3]. GP is an extension of the Genetic Algorithm (GA) and the basic steps for the GP algorithm are similar to the GA steps. ABCP is the recently proposed automatic programming method based on Artificial Bee Colony (ABC) algorithm [4]. In this paper, the ABCP image descriptor was first proposed and the success of the extracted models in the texture classification problem was evaluated. In recent years, automatic programming methods have been used more frequently in image processing problems. [PERSON] [5] used GP to classify feature vectors derived from images into categories. A domain adaptive learning method based on Multi-Objective Genetic Programming (MOGP) was proposed to automatically generate field-adaptive global property descriptors for image classification in [6]. GP was suggested for feature detection in [7]. The method separated the image into the sub-regions, extracted the Speeded up Robust Features (SURF) points and achieved successful classification results using the Support Vector Machine (SVM) classifier. [PERSON] et al. proposed an image classification method using transfer learning and GP [8]. The GP trees are extracted by the fragments of the transfer learning improved classification performance producing more useful initial population. In [9], they introduced new GP method that automatically differentiated the regions in the image and sorted them out by extracting Histograms of Oriented Gradients (HOG). All stages of the method were optimized with wide search space. [PERSON] et al. proposed a Two-Tier GP (Two-Tier GP, Two-Tier GP-line, and Two-Tier GP-mix) with three different variations [10]. Instead of using them as input values, they defined special functions that can convert line pixel values into a single numeric value, and in the other tier, classification was made by using the outputs of special functions. Two Tier GP-mix was considered the most successful method because of the flexibility of the window range. Comparative studies of ABC with Particle Swarm Optimization, GA and other evolutionary computational algorithms have shown that ABC has better performance in terms of achieving local and global optimum results in engineering problems [11]. ABC is an algorithm that is successfully applied in image processing, especially in image clustering [12], image registration [13], pattern recognition [14], image segmentation [15; 16; 17; 18; 19], and image classification [20]. ABC algorithm was used in the diagnosis of computed tomography images in [20]. The images are classified with k Nearest Neighbor (k-NN) and SVM classifiers by selecting features with ABC. The ABC-SVM hybrid is highly successful. The ABCP algorithm has been proposed as an ABC-based method that improves functions on symbolic regression [4]. This is the first paper that observes the performance of the ABCP algorithm in texture classification and extracts mathematical models. Nowadays, due to its simplicity of calculation and its robustness against light changes, LBP-based descriptors have been extensively researched due to its importance in various fields such as image classification problem, pattern recognition and computer vision [21; 22; 23; 24; 25]. [21] was proposed a GP-based LBP image descriptor using one instance per class.