Research On Key Techniques Of Image Classification For Semantic Extraction

With the rapid development of digital imaging technology, the amount of image is increasing rapidly. It's difficult to find the user-wanted images from huge mount of image data. So how to organize these images and retrieval a special image from the mass database efficiently and effectively has become a major issue. Image classification is an important and challenging task in this field and is attracting more and more attention. But there is so much difficulty in image classification task for semantic extraction, how to generate a more effective image classification method is still an open problem.In this dissertation, some key techniques of image classification for semantic extraction have been explored, which include feature extraction, multi-feature fusion and multi-class classifier. The original contributions of this thesis can be described as follows:1. An image regional latent semantic distribution feature is proposed for scene classification. The core of this feature extraction method is how to get the regional latent semantic. Firstly, an image block collection is generated by using spatial pyramid subdivision method on training image collection. Then the Probability Latent Semantic Analysis method is used on the image block collection to mine the regional latent semantic. Finally, the image regional latent semantic distribution feature is defined by uniting the probability value of each regional latent semantic in each image block region. Comparing with other intermediate semantic features, this feature has used the distribution of regional semantic to improve image classification performance, as well as it reduces the load of people. Experiment results show that this feature has satisfactory classification performance on a large set of 13 categories of complex scenes.2. A multi-feature fusion model based on kernel combination and its optimization algorithm is proposed. In this classification model, multi-feature fusion is completed by a convex combination of feature kernels, and each feature kernel corresponds to an image feature. Then, the problem of how to fuse image features excellently has become another problem which is how to optimize the combinational kernel. To solve this problem, multiple kernel learning can be used. But the multiple kernel learning methods in existence have only maximized the between-class variance. Based on Fish rule, an excellent classifier should maximize the between-class variance and minimize within-class variance. To satisfy this rule, a new multiple kernel learning method is proposed and has been used to optimize multi-feature fusion model. The experimental results show that the proposed method can finish feature selection and feature fusion at the same time, and has higher classification accuracy. 3. A multi-class method cascading minimum distance classifier in model subspace is proposed. Firstly, this method uses minimum distance classifier to get a litter class collection, and then multi-class SVM classifier is used to classify on this collection. To improve the classification performance, a minimum distance classifier based on model subspace is proposed. To guarantee the classification speed and precision of minimum distance classifier on model subspace, a new distance measure learning method which based on sparse restriction of distance weight is proposed. By this method, an optimization model subspace and an optimization distance can be generated at the same time. The experiments on Caltech256 show that the proposed method can guarantee the classification speed and precision.4. A new inter-class similarity combing feature distribution and class semantic is proposed, and this measure has been used to automatic generation of class hierarchy. In the measure based on feature distribution, the train data has been clustered firstly, and then the prior probability distribution of cluster has been used to describe the feature distribution of each class, finally a distance based probability distribution is used to get the inter-class similarity. On the other side, the semantic similarity of class word has been computed based on WordNet. The total inter-class similarity has been computed by combining these two measures and a class hierarchy has been automatically generated based on this measure by spectral clustering algorithm. Comparing with the method only using one measure, the proposed method has higher classification performance.