The Study On Image Classifition And Image Annotation

With the development of multimedia technology and the popularization of Internet, people can acquire multimedia information in large amount. How to retrieve the images from image database precisely and efficiently has been an important issue in the field of image retrieval.Content-Based Image Retrieval(CBIR) extracts visual features as retrieval features, such as color, texture and shape, etc. For the existence of semantic gap between low-level image features and human understanding to images, CBIR can't get satisfied retrieval results. Classifying images into reasonable categories using low-level features or annotating images will greatly improve the performance of CBIR systems. This thesis does a study of image classification and image annotation. The main contributions of this thesis are as follows:1. Propose a method of rotation invariant texture classification using Gabor transform and Support Vector Machine(SVM). To make sure the classifier knows nothing about the characters of rotated images, we create the training set from the subimages from the top half of none rotation image. The subimages from the foot half of rotated images are grouped to the testing set. This method is tested on Brodatz and UIUCTex datasets and the experimental results demonstrate that it is effective and efficient. The precision can be as high as 100% in some classes.2. Propose a method of image classification based on MPEG-7 color and texture descriptors, using SVM as classifier. For there are several classes in image dataset, the approach constructs the multi-class SVM with the help of multi-class classification strategy. Image features are extracted using MPEG-7 Experimentation Model software. The experiment with Corel 1K utilizes several color and texture descriptors. Classification precision and time complexity are given.. The results show that if we properly fuse the MPEG-7 descriptors the higher precision can be achieved.3. Propose a method of image annotation using MPEG-7 descriptors and SVM The image features are global features based on MPEG-7 color and texture descriptors. The method builds a binary SVM according to each word. For there are a lot of words usually, the method constructs the multi-class SVM with help of multi-class classification strategy. Therefore, this multi-class SVM establishes a mapping from images to words. The output of SVM classifier is modified to posterior probability form so we can get the probability estimates. In the experiment with Corel 5000 dataset, the method use Porter stemming algorithm as the first step. By eliminating the words with so few images, 82 words are used to build SVM classifier. The mean per-word precision and recall as well as mean per-image precision and recall are adopted for evaluating annotation effectiveness.