Research On Key Issues Of Image Analysis With Ensemble Learning

With the development and popularization of Internet technology, the data of images and videoshave increased exponentially. The valid inference and learning algorithms for the computer visiontasks are very important, which would help people to obtain useful information and knowledgeconveniently. Recently, ensemble learning, an effective machine learning method, has been widelyused in various fields and attracted increasing attention especially in computer vision. The state ofthe art of ensemble learning are summarized in this paper. We mainly focus on the three applicationswhich include image representation, segmentation and classification. By analyzing the existingproblems involved in the above tasks, we explore ensemble learning method to improve theperformance of feature representation, segmentation label and object classification. The mainachievements and innovations can be concluded as follows:(1) We focus on the problem of image feature representation. A feature incremental learningmethod in independent subspace in the original unlabeled data space is proposed to get structuralfeature element matrix and form effective feature space representation. At the same time, a distancemeasuring method based on AP clustering is provided and the outlier of sample is defined which candetect outlier in the original data space to help the sample selection. This method can be used notonly in image classification, but also in image retrieval. The experiments show that our method canfind outliers effectively and achieve better performance than other popular feature representation,learning methods, and classification. Meanwhile, this paper also study multi-kernel and multi-featureensemble. The experiments show that it can get more effective results on scene classification.(2) Unsupervised clustering ensemble for image segmentation is proposed. As the same singlecluster will get different performance under different problems and the different clusters will getdifferent performance under the same problem, the clustering ensemble is used to improve theaccuracy and generalization of clusters. Image segmentation is defined as pixel level labelingproblem and clustering ensemble algorithm is applied to the task of image segmentation. Aclustering ensemble mechanism for image segmentation is provided. By integrating multi-clusterimage segmentation results, the differences of single cluster are aligned. Then, with weighted votingstrategy, the ensemble segmentation results are combined. The experiments on UC Berkeley imagedataset show that the segmentation results by ensemble clustering re consistent with humanperception and better than single cluster. The estimate indicators also have been improved greatly.(3) Classfier ensemble by transfer learning is studied to solve the problem of imageclassification. By design the transfer learning strategy, the knowledge is shared among related tasks.It can extract knowledge from one or multi-source domain to solve the problem in target domain.The knowledge in different domains, tasks and distributions can be broadcasted by reusing thetraining data. The generalization of computing can be improved when solving the problems uding few samples or the training and testing data set with different distributions. We focus on how to usetransfer learning method to solve the image classification under the above cases. Covariate shift isincorporated into the loss function to cope with the distribution differences between source domainand target domain. Additionally, the transferability of source domains is evaluated and eliminateirrelevant source domain gradually. Our method enhances the effectiveness in choosing availablesource domain, avoids negative transfer and promotes computational efficiency. Experiment resultsshow the proposed algorithm can achieve higher classification accuracy by using less training data.