Object Detection Algorithm Based On Visual Memory:a Feature Learning And Feature Imagination Process

Object detection is one of the fundamental problems in computer vision and it focuses on detecting object from images. Object detection is widely used in many applications, such as video surveillance, human-computer interaction, intelligent traffic management, driver assistance system and medical image analysis. Today, most object detection methods prefer to simplify object detection into a binary classification problem:determine whether there is the set type of object in the sliding window or not. Thus, the main steps of an object detection method contain the construction of object model, object search strategy and the classification of object. For the reason that, image will change a lot when variable illumination, background interference or object occlusion happens. At this time, object detection method can not detect object in the image well and the detection process will be more time consuming. However, human visual system can complete the object detection well when facing the same problem. To improve the performance of object detection, it’s an important way to research the mechanisms used by human visual system when detecting object and construct an object detection algorithm witch possessed the intelligence of human visual system.On the basis of the concept of feature learning and feature imagination, this dissertation focuses on the mechanism of human visual memory and constructs the feature learning and feature imagination model based on human visual memory. Then we use this model to propose an object detection algorithm. Tested on some datasets, our object detection algorithm has been proved that it can speed up object detection without any decline in detection accuracy. On the other hand, this dissertation also discusses video synopsis and indexing method and image quality assessment method based on object detection, and analyzes their performance based on experiment. The main contents of this dissertation are listed as below:1. We study the human visual memory mechanism and describe it as a process of feature learning and feature imagination. We also describe and analyze two types of the saved feature of visual memory. The saved features of visual memory include mode of processing features and essential features for visual memory. The mode of processing features extracts visual features to make the search of object convenient, and the essential features of visual memory are the important features to classify the object. Thus we construct feature learning and feature imagination model based on visual memory.2. In order to simulate the mode of processing features in visual memory, we construct a saliency detection algorithm based on human visual selective attention mechanism. This algorithm uses scale, color and position information of object to propose a hierarchical saliency calculation method. And then, we use a binarization method based on adaptive threshold to binarize the saliency map and extract candidate detection area. Thus we reduced the search area of object detection and speed up object detection.3. Based on the feature learning and feature imagination model, we propose an object detection algorithm based on visual memory. The algorithm use deformable part model as the detector and simulate essential features of visual memory by extracting edge and intensity features of object. Our object detection algorithm can speed up object detection without any decline in detection accuracy.4. This dissertation proposes a video synopsis and indexing algorithm based on object detection. The video summary generated by our algorithm will not lose any spatial and temporal information of object, and can be retrieved by the property information of object. We analyze the performance of our algorithm based on the experiment in the video surveillance system.5. Image quality assessment is one of the hot research areas in the field of image processing. For the reason that human being is the final receiver of the image, the image quality assessment should match the characteristics of human visual system. The image quality assessment algorithm proposed in this dissertation extracts detector score and saliency score of image to describe the image clarity, complexness of background and completeness of the object in the image, and get the final image quality assessment. Tested on some datasets, our algorithm is proved to meet some characteristics of the human visual system.