图像数据的视觉显著性检测技术及其应用
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摘要
图像是信息社会的主要数据资源,海量的图像数据给高效智能信息处理带来了挑战。我们注意到,人们关心的内容通常只是整幅图像或整段视频中很小的一部分,因此,有必要直接检测出它们,以获得高效的处理结果。这种处理思想源自于人类视觉的选择性注意机制和感知组织原则。由此,我们需要面对如下问题:如何利用视觉显著性的感知原理?如何描述和区分图像信息中可能存在的多种显著性事件?如何将这些心理学原理有效地引入图像分析进程?如何从静态图像或视频序列中快速检测用户关心的显著区域或事件?本论文围绕其展开了研究。
论文第一部分集中讨论了视觉显著性检测的基本处理思想。首先,回顾了认知心理学的相关理论,讨论了视觉显著性和图像内容之间的对应关系,提出了一种基于内容相关度的视觉显著性表述策略,将图像显著事件分为弱相关事件和强相关事件两类;继而,分析了注意与组织的层次协作关系,提出了一种图像显著内容的层次描述与理解框架;接着,提出了一种基于泛化注意的图像视觉显著性检测模型,用以将选择性注意机制融入到整个图像处理过程中。
论文第二部分集中研究了面向图像数据的视觉显著性检测方法。首先,提出了一种基于注意的显著区域分割及其特征学习改进算法,用以解决区域图像检索中的显著基元提取与描述问题。其后,研究了遥感图像目标识别的应用问题,(1)提出了一种人造目标检测模型和一种区域分割算法,用以解决人造目标候选区的聚焦问题。该模型是层次化结构感知的,区域分割是水平集演化;(2)构建了一种基于结构编组的人造目标分析框架、线结构基元的提取和编组方法,用以解决人造结构的感知组织问题;(3)提出了一种基于显著基元分类感知与编组的遥感道路检测和提取算法。随后,提出了一种基于空时注意的视频显著事件检测模型,并用于视频火焰事件检测和火焰显著区域的提取。
论文最后提出了一种图像数据的视觉显著性检测技术实验系统的设计方法,讨论了其可能的潜在应用和扩展问题。
论文中提出的各种模型和方法应用于多种类型的真实图像和视频,获得了预期的试验结果,体现出一定的可行性和适应性。
Images are the primary data resource in information society. Voluminous image datum results in the critical challenge for the high efficient information processing intelligently. We notice that the content that a person is interested in is often occupied a small part of an image or a period of video. It is necessary directly to detect the interested areas for high efficient processing results. The processing idea stems from the selective attention mechanism and the perceptual organization principle in the human vision system. Thus, the following items should be dealt with: How to utilize the perception principles of visual saliency? How to describe and distinguish the various saliency events contained in images? How to introduce above psychological theories into the procedure of image analysis effectively? How to extract the salient regions or events rapidly from an image or a video period, which are interested by almost users? This dissertation focuses on these aspects.
The first part of this thesis emphasizes on the framework design for visual saliency detection. Firstly, after discussing the relation between visual saliency and image contents based on the theories of cognitive psychology, a new strategy for representing visual saliency is proposed based on content-correlation, by which image salient events can be divided into two classes, low correlative and high. Secondly, a hierarchical framework for describing and understanding image saliency is presented by analyzing the cooperation between attention and organization. Thirdly, an image saliency detection model is developed based on the general attention in order to put selective attention mechanism into the whole procedure of image processing.
The second part of this thesis studies on the methods of visual saliency detection for image datum. Firstly, an improved attention driven algorithm for salient region segmentation and feature learning is proposed to obtain salient elements and description for region-based image retrieval. Secondly, the applications on target recognition in remote sensing images are researched. (1) A hierarchical model on man-made object detection is built up and a level set evolution algorithm for man-made region segmentation is developed to focus on salient man-made candidate areas. (2) a man-made object analysis framework based on structure grouping and a method for extracting and grouping line-like structural elements are adopted in order to implement perceptual grouping of man-made configuration. (3) A road detection and extraction method based on classified salient element perceptual grouping is developed. Then a video event detection model based on spatial-temporal attention is presented, and is applied to detect fire events from video images by extracting fire-like salient regions.
The final part of this thesis offers a general design method of an experimental system on visual saliency detection in image data, and discusses some potential applications and other relative extend items.
The models and algorithms developed in the thesis are applied to various real images and video and the expected results are obtained. It has some feasibility and adaptability.
论文第一部分集中讨论了视觉显著性检测的基本处理思想。首先,回顾了认知心理学的相关理论,讨论了视觉显著性和图像内容之间的对应关系,提出了一种基于内容相关度的视觉显著性表述策略,将图像显著事件分为弱相关事件和强相关事件两类;继而,分析了注意与组织的层次协作关系,提出了一种图像显著内容的层次描述与理解框架;接着,提出了一种基于泛化注意的图像视觉显著性检测模型,用以将选择性注意机制融入到整个图像处理过程中。
论文第二部分集中研究了面向图像数据的视觉显著性检测方法。首先,提出了一种基于注意的显著区域分割及其特征学习改进算法,用以解决区域图像检索中的显著基元提取与描述问题。其后,研究了遥感图像目标识别的应用问题,(1)提出了一种人造目标检测模型和一种区域分割算法,用以解决人造目标候选区的聚焦问题。该模型是层次化结构感知的,区域分割是水平集演化;(2)构建了一种基于结构编组的人造目标分析框架、线结构基元的提取和编组方法,用以解决人造结构的感知组织问题;(3)提出了一种基于显著基元分类感知与编组的遥感道路检测和提取算法。随后,提出了一种基于空时注意的视频显著事件检测模型,并用于视频火焰事件检测和火焰显著区域的提取。
论文最后提出了一种图像数据的视觉显著性检测技术实验系统的设计方法,讨论了其可能的潜在应用和扩展问题。
论文中提出的各种模型和方法应用于多种类型的真实图像和视频,获得了预期的试验结果,体现出一定的可行性和适应性。
Images are the primary data resource in information society. Voluminous image datum results in the critical challenge for the high efficient information processing intelligently. We notice that the content that a person is interested in is often occupied a small part of an image or a period of video. It is necessary directly to detect the interested areas for high efficient processing results. The processing idea stems from the selective attention mechanism and the perceptual organization principle in the human vision system. Thus, the following items should be dealt with: How to utilize the perception principles of visual saliency? How to describe and distinguish the various saliency events contained in images? How to introduce above psychological theories into the procedure of image analysis effectively? How to extract the salient regions or events rapidly from an image or a video period, which are interested by almost users? This dissertation focuses on these aspects.
The first part of this thesis emphasizes on the framework design for visual saliency detection. Firstly, after discussing the relation between visual saliency and image contents based on the theories of cognitive psychology, a new strategy for representing visual saliency is proposed based on content-correlation, by which image salient events can be divided into two classes, low correlative and high. Secondly, a hierarchical framework for describing and understanding image saliency is presented by analyzing the cooperation between attention and organization. Thirdly, an image saliency detection model is developed based on the general attention in order to put selective attention mechanism into the whole procedure of image processing.
The second part of this thesis studies on the methods of visual saliency detection for image datum. Firstly, an improved attention driven algorithm for salient region segmentation and feature learning is proposed to obtain salient elements and description for region-based image retrieval. Secondly, the applications on target recognition in remote sensing images are researched. (1) A hierarchical model on man-made object detection is built up and a level set evolution algorithm for man-made region segmentation is developed to focus on salient man-made candidate areas. (2) a man-made object analysis framework based on structure grouping and a method for extracting and grouping line-like structural elements are adopted in order to implement perceptual grouping of man-made configuration. (3) A road detection and extraction method based on classified salient element perceptual grouping is developed. Then a video event detection model based on spatial-temporal attention is presented, and is applied to detect fire events from video images by extracting fire-like salient regions.
The final part of this thesis offers a general design method of an experimental system on visual saliency detection in image data, and discusses some potential applications and other relative extend items.
The models and algorithms developed in the thesis are applied to various real images and video and the expected results are obtained. It has some feasibility and adaptability.
引文
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