Research On Structral Texture Feature Descriptor And Its Applications

The wide application and rapid development of the Internet leads to the explosive growth of network data. As a commonly used way of information expression, images need not much words to describe the information contained in images. Images can be simply understood by human and contain lots of information. With the growing amount of image data, the image processing technology is widly used in medical, military, agriculture, industry, service, and other industries. Applications such as medical image analysis, military targets detection and recognition, plant morphological characteristics measurement, traffic monitoring,3D-modeling, restaurant recommendation, etc. The demand of various applications for digital images is becoming more exuberant and the requirements of image analysis technology are also getting more higher. How to analyze these images and apply them in different applications is an important topic.As the most effective way to describe the image, features are very important in image processing. Features such as color, shape and texture are the mostly used feature in digital image processing. Among these features, texture as a form of human visual system on the surface performance of the perception. Through the calculation of each pixel in the image grayscale or color variation and distribution characteristics of changes, to response the roughness of surface, in line with the direction characteristics and rules of object surface. Texture analysis technique has been widely used in face recognition, the agricultural product quality supervision, road traffic monitoring, remote sensing image processing, content-based image retrieval, robot vision and many other fields. For different applications and different image types, the requirements for image texture are different. At the same time, because of the complexity of texture structure itself, texture analysis technology has become a high difficulty of Science in the field of digital image processing.In recent years, many researchers have proposed various texture feature extraction methods, they can be divided into four main categories:statistical based methods, model based methods, structure based methods and the methods based on signal processing.Structure based methods has the advantages of small calculation amount, low dimensionality and rotation invariance, etc., has been widely used in various fields of researches and applications. Traditional texture features based on structure hasn’t consider the variation of texture direction and spatial distribution characteristics, which can not fully express the texture directional features and spatial distribution of texture features in image.In some applications such as image retrieval, these features not only can not correctly distinguish the different classes of images, but also unable to obtain better retrieval results. Therefore, the further study of structure based texture features extraction is very important, the improvement of the existing methods has very high research value in theory and application, which not only can improve the validity of the texture feature extraction, but also provide theoretical basis for different applications.This paper firstly describes the background and significance of structural texture features extraction, discusses the related field work and introduce the basic idea and research methods theoretically. Secondly, this paper focus on the method of feature extraction, analyze the shortcomings of traditional structural texture extraction methods on the texture variation direction and spatial distribution, propose the new structure texture feature descriptor based on directional characteristics and spatial distribution. Moreover, the image retrieval algorithms based on the feature descriptor are proposed. Finally, the analysis of traditional optical remote sensing image ship detection problems and shortcomings in the remote sensing image has been made, the improvement of sea land segmentation results combined with texture and structure is proposed in this paper to improve the effect of ship detection.The main contribution of this paper is shown as:(1) To solve the problem of unable to extract directional texture feature of local binary pattern, a new texture feature descriptor based on the texture directional variation is proposed. Through the calculation of pixel gray variation on different directions, the feature co-occurrence matrix is constructed to reflect the local gray-scale variation, finally through the statistics of different gray patterns, directional feature and amplitude characteristics are proposed to supplement the local binary pattern texture features, which can effectively improve the overall texture feature description;(2) Aiming at the problem of unable to extract the spatial distribution characteristics of texture, new structural texture feature descriptors named as local spatial binary pattern and local spatial distribution pattern are proposed. Based on these two descriptors, multi-scale local spatial binary pattern and completed local spatial distribution pattern are also proposed. Local spatial binary pattern based on the gray-scale variation pattern between pixels, and calculated to reflect the distribution of gray-scale variation. Moreover, with multi-scale considered, the texture feature can be expressed more comprehensively. Local spatial distribution pattern calculates the gray- level variation pattern between different pixels on different directions. Completed local spatial distribution pattern not only extract the local spatial distribution pattern of original gray image, but also extract the local spatial distribution pattern of gradient image and filtered image from the original iamge, which can improve the completity of texture feature extraction;(3) Based on the advantages of simple calculation, low computation and comprehensive feature description, applying the structral texture feature descriptors in image retrieval algorithms. To verify the effectiveness and validity, two image retrieval algorithms namely based on multi-scale spatial local binary pattern and completed local spatial distribution pattern are proposed. Experimental results show that, compared with the similar methods, the proposed algorithms has better retrieval results, which can both improve the average recall and precision results;(4) To improve the sea-land segmentation of traditional methods, this paper propose a new segmentation algorithm based on local binary patterns. By calculating the local binary patterns and the integrated feature map to segment the optical remote sensing images, and combined with the traditional gray sea land segmentation results, better segmentation results can be obtained. The experimental results show that the algorithm can ensure the high accuracy of the ship detection, which can also greatly reduce the false alarm rate of ship detection.In this research, the deficiencies of the current structural texture features are analyzed. Structral texture features of directional variation and spatial distribution are proposed to impress the research of structral texture feature extraction, which provides the theoretical basis for the texture and structure of applications in various fields. Moreover, the application of structural texture features in image retrieval and the ship detection of optical remote sensing image are discussed, which also provides a new way for more fields in the further application and development of structural texture features.