In order to solve the problems of poor robustness and high mismatch rate caused by the noise, illumination and scale in the image feature matching process, a feature matching method based on Weighted Similarity Measurement (WSM) was proposed. At first, FM_GMC (Feature Matching based on Grid and Multi-Density Clustering) algorithm was adopted to divide image into several feature clustering blocks. Secondly, in each feature clustering block, the edge feature points were attracted by Canny and descripted by Scale-Invariant Feature Transform (SIFT). Thirdly, the similarity measurements were performed on the Hausdorff distance of spatial context information between feature clustering blocks, the Euclidean distance between appearance descriptors of image feature points and Normalized Cross Correlation (NCC) by using weighting methods. Finally, the similarity measurement results were further optimized according to Nearest Neighbor Distance Ratio (NNDR), so as to determine the feature matching result. With the ancient architecture images as the dataset, the experimental results show that the WSM method has an average matching precision of 92%, and is superior to commonly matching algorithms on matching number and matching precision. Therefore, the effectiveness and robustness of WSM method are verified.

To generate an effective visual dictionary for representing the scene of images, and further improve the accuracy of semantic annotation, a scene annotation model based on Formal Concept Analysis (FCA) was presented by means of an abstract from the training image set with the initial visual dictionary as a form context. The weight value of visual words was first marked with information entropy, and FCA structures were built for various types of scene. Then the arithmetic mean of each visual word's weight values was used to describe the contribution among different visual words in the intent to the semantic, and each type of visual vocabularies for the scene was extracted from the structure according to the visual vocabularies thresholds. Finally, the test image was assigned with the class label by using of the K-nearest method. The proposed approach is evaluated on the Fei-Fei Scene 13 natural scene data sets, and the experimental results show that in comparison with the methods of Fei-Fei and Bai, the proposed algorithm has better classification accuracy with β=0.05 and γ=15.