出版社：International Institute for Science, Technology Education
摘要：Image segmentation and Image Classification are two fundamental tasks in computer vision. In this thesis, a novel segmentation algorithm based on a deformable model and robust estimation is introduced to produce reliable segmentation results. The algorithm is extended to handle touching objects and partially occluded image segmentation. Although current conventional image classification methods have been widely applied to realistic problems, there are some issues with their implementation, including unsatisfactory results, poor classification accuracy, and a lack of adaptive capacity. This approach has been used to isolate the two processes of image feature extraction and classification into two stages. The deep learning model possesses a strong learning capability, which enables it to incorporate the feature extraction and classification processes, thereby improving the image classification accuracy. This thesis explores various machine learning methods to improve the model's performance. The primary objective is to discover the accuracy of the various networks on the datasets and to evaluate the consistency of each of these deep learning predictions. Nonetheless, there are limitations to this approach: first, it is difficult to perform accurate approximation in the advanced model. The second point is that the deep learning model comes with poor accuracy in its classifier. So, this paper introduces the idea of using different datasets and models of the deep learning network and comprehensively utilizes it to determine the best test accuracy for the images. In this paper, a deep neural network primarily based on Keras and TensorFlow is deployed using python. The two datasets are used to compare to determine which has the maximum accurate and fine time for processing. And a VGG-16 model method based on the optimized kernel function is proposed to replace the classifier in the deep learning model. The experimental results show that the proposed method not only has higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effectiveness, thus further improving image classification accuracy.
