In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature e...In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.展开更多
This paper presents a universal scheme （also called blind scheme） based on fractal compression and affinity propagation （AP） clustering to distinguish stego-images from cover grayscale images, which is a very chal...This paper presents a universal scheme （also called blind scheme） based on fractal compression and affinity propagation （AP） clustering to distinguish stego-images from cover grayscale images, which is a very challenging problem in steganalysis. Since fractal codes represent the ＂self-similarity＂ features of natural images, we adopt the statistical moment of fractal codes as the image features. We first build an image set to store the statistical features without hidden messages, of natural images with and and then apply the AP clustering technique to group this set. The experimental result shows that the proposed scheme performs better than Fridrich＇s traditional method.展开更多
基金Sponsored by the National Natural Science Foundation of China (Grant No. 51075083)
文摘In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.
基金supported by the National Natural Science Foundation of China under Grant No. 61070208the Postdoctor Foundation from North Electronic Systems Engineering Corporation
文摘This paper presents a universal scheme （also called blind scheme） based on fractal compression and affinity propagation （AP） clustering to distinguish stego-images from cover grayscale images, which is a very challenging problem in steganalysis. Since fractal codes represent the ＂self-similarity＂ features of natural images, we adopt the statistical moment of fractal codes as the image features. We first build an image set to store the statistical features without hidden messages, of natural images with and and then apply the AP clustering technique to group this set. The experimental result shows that the proposed scheme performs better than Fridrich＇s traditional method.
