| Technological progress is always accompanied by a rapid proliferation of image data. And size of image data is also becoming larger and larger. Facing such vast amounts of image data, how to complete different kinds of image analysis tasks effectively and fast is a hotspot which people concern about. However, in many image analysis tasks, such as image retrieval, scene analysis, surveillance systems, objects detection, and tracking, people pay more attentions to some special regions or objects which interest them. These objects or regions of interest (ROI) usually are only a small part of an image. In traditional image analysis methods, each position of one image is usually treated with the same priority and all regions are processed orderly and sequentially. Such all-sided processes increase the complexity of analysis tasks and become time consuming, computation consuming. So there is a need to find effective and robust methods that are adaptive to different image analysis tasks to locate ROI rapidly. In recent years, many researchers have found that the Human Vision System (HVS) can focus on several salient objects or regions fast in a complex scene, and HVS prefers to process them first. This is called "Visual Attention Mechanism", and those salient objects or regions are called "Focus of Attention" (FOA). Obviously, it is necessary to introduce this mechanism to the filed of image analysis. It can provide the ability of selection for image analysis process, help to make a suitable plan to allot our limited resource of computation, and also improve the efficiency of our existing image analysis systems.In psychology, researchers think that objects which make more visual stimuli or novel stimuli and some objects which people expect can attract more human's attention. These are called "stimulus-driven capture" and "motive selection". Accordingly in computer vision there are bottom-up attention and top-down attention. Bottom-up attention models are data-driven and independent of image analysis tasks, while top-down attention models are intention-driven and dependent on image analysis tasks. In this paper, we focus on bottom-up attention models. So our following research works mainly include two parts:visual saliency modelling and salient object detection. First, we introduce the features of visual attention mechanism and the fundamental theories, also analysis the previous methods and classic models. Then we construct a bottom-up visual attention model for natural scenes. Based on our visual attention model and saliency computation, several different approaches are proposed for salien object detection in some image analysis applications.The contributions of the dissertation are listed as follows:(1) A visual attention model is proposed for natural scenes, including different global visual saliency measurements for different features, a strategy for dynamic feature evaluation and combination and the simulation for location shift of FOA. Comparing with previous models, this model is better at keeping object-integrality in semantic and object's contours, and more similar to real human visual attention process.(2) An approach for object-video retrieval based on visual saliency is proposed. It constructs feature vectors based on salient objects in videos, not on frames to neglect the effect of background. In addition, we extend our visual attention model by adding mostion analysis for time sequence images and an approach for salient object detection in time sequenced images is proposed. Experimental results indicate our approach is effective and robust.(3) The visual saliency computation is applied onto remote sensing image processing tasks in this paper. Based on saliency computation, a ship detection approach with complex sea surface background and a change detection method with noisy background are proposed. Comparing with the traditional segmentation methods based on statistics, our approaches are more effective and more robust to noise, brightness and contrast.
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