Research On Image Scene Recognition Method Considering Scene Complexity

With the rapid development of computer and Internet technology,computer images play an increasingly important role in various fields of current social life.We produce massive image data almost every day.It is a great challenge to take advantage of image data due to their high magnanimity and complexity.Image retrieval is the most basic way of image application,but most of the image retrieval is based on the manual way to describe the overall scene information of the images.therefore,the accuracy of marking information is influenced a lot by subjective factors,and the arbitrariness of marking content is strong.Faced with such a large amount of image data,the method of artificial annotation has far exceeded our power,making the demand of using computer to realize automatic image annotation urgent.But fortunately scene recognition has become the key technology to achieve automatic marking of images,which has made it turn into a new research hotpot in recent years.Compared with the artificial scene using strong structural features,the type of features that the natural scene present are related to the content and complexity of the scene,so we can not simply apply the method of the artificial scene recognition to the natural scene recognition Considering these situations,an evaluation model of image scene complexity is proposed in this paper,and the evaluation results are used as the basis for the selection of different scene recognition methods.Furthermore,a scene recognition method based on multi-feature fusion and result feedback is studied for simple natural scene images.And it takes into consideration the spatial relation for the more complex natural scene images.The main research contents and achievements of this paper include the following parts:(1)Evaluation model of image scene complexityWe select the positive correlation parameters such as information entropy,energy and entropy,the number of corner points and the edge information of the image to establish the image scene complexity evaluation model in this paper.The K-means clustering method is used to obtain the characteristic clustering center with different complexity levels.And the complexity of the image scene is evaluated according to the distance between the hierarchical complexity clustering centers and the image(2)Simple natural scene recognition based on multi-feature fusion and result feedbackFor simple scene images,a scene recognition method based on multi-feature fusion is studied,including the method of extracting and merging the global features such as color,texture and shape.The feedback mechanism is introduced to the scene recognition method in order to realize the automatic configuration of multi-feature weight by adaptive learning and adjustment.(3)Complex natural scene recognition considering spatial relationsFor complex scene images,we study the scene recognition method involving spatial relations.First we need to divide the sub-region of the image scene,extract and cluster the features of each sub-region,and form the visual keywords of each sub-region,and finally construct the spatial visual dictionary of the whole training image area.In the process of scene recognition,SVM classifier is used to measure the similarity between sub-regions of the corresponding space.The recognition results of the image scene are obtained by combining the similarity degree of all the spatial sub-regions.Based on the theories and methods above,the paper establishes corresponding experiment and analysis,the experimental results show that the scene recognition method considering scene complexity proposed in this paper has achieved better recognition accuracy and low recognition time-consuming in simple natural scene image sets as well as complex natural scene image sets.The experimental results verify the validity of the theory and method proposed in this paper.