Research On Scene Classification Technologies With The Local Region Description Feature And Probabilistic Latent Semantic Analysis Model

The research of scene image classification is that how to make computer vision systems to classify the image sets which contain semantic information, according to understanding and discriminating the scene image of human. Scene classification is the core issue in the computer vision and image understanding research area, which could organize and process large mount of image data, and then used to retrieval or scan images reasonably or effectively. In consideration of the relationship between image and text, it is significant to make the bag-of-words model which is used to text corpus research area to describe the image and use the probabilistic latent semantic analysis model to classify the image. For the mutual restriction between the effectiveness and complexity of the image feature extraction algorithm, we employ the following research:First of all, we built the Edge Improved Local Binary Pattern (EILBP) feature of the local region which center point is formed by dense sampling the edge of gray image, it is simple, stable, and it can give a reasonable description about the gray image which contain rich contour information, and then we can obtain potential semantic of image by Probabilistic Latent Semantic Analysis (PLSA) model, after that we can accomplish the scene classification by K-nearest Neighbours Classier (KNN) classifier. The experiment results show that this method could achieve a higher accuracy, especially perform well in multi-edge gray images.Then, we construct the Edge Improved Center Symmetric Local Binary Pattern (EICS-LBP) feature of the local region which center point is formed by dense sampling the edge of gray image, it is produced based on the EILBP feature and the symmetry. For the color information of the color image, we construct the feature of statistical edge domain color pairs, it can describe the edge domain color pairs information of the local region. After that the new visual vocabulary is created by linear combination of the two species of visual vocabulary which are formed by clustering the corresponding features from dense sampling regions respectively. At last, we can obtain potential semantic by extended PLSA model and accomplish the scene classification by KNN classifier. The experiment results show that this method could achieve a higher accuracy, especially perform well in multi-edge color images.Finally, the traditional visual words method considers nothing about the reliance among of features, it couldn't express the theme of image well. To overcome its defect, we propose the contextual visual features based on the EICS-LBP feature and the statistical edge domain color pairs feature of the color image. At last, we can obtain potential semantic by extended PLSA model and accomplish the scene classification by KNN classifier. The experiment results show that this method could achieve a higher accuracy, especially perform well in color images which contain multi-edge and rich contextual information.