With the development of complex network, community detection is widely used. Louvain algorithm is a community detection algorithm based on modularity, which has relatively reliable partition results and high efficiency, and can detect the hierarchical community structure. This paper proposes the following improvements for Louvain algorithm: 1. Introduce WCC algorithm, so that Louvain can be applied to disconnected graphs. 2. In the case that leaf nodes and all neighbor nodes are in the same community in the iterative process, direct integration is carried out to reduce redundant calculation. 3. After the calculation of the first layer, a correction process is introduced to make the results of the bottom partition more effective when doing downstream tasks. Compared with the original algorithm, the result has better partitioning effect and faster processing speed.
K-truss is a dense subgraph based on clique. Finding the largest k-truss in a graph is an important problem in graph analysis. The truss decomposition algorithm is widely used in finding k-truss because of its simplicity and ease of implementation. With the explosive growth of graph data scale, the traditional truss decomposition algorithm causes a lot of redundant computation and is no longer applicable. The kmax search strategy that combines large, medium and small steps, realizes subgraph prediction through k-core, reduces the data size, uses large, medium and small steps to retrieve, reduces the number of iterations, and eliminates some redundant calculations. However, the middle step and small step use fixed values, and the number of iterations is too many on some scale graphs. This paper proposes a dynamic adaptive step size method, and uses the clique to analyze the intermediate results, ending the iterative process early and improving the execution efficiency. The experimental results show that compared with the existing kmax search strategy combining large, medium and small steps, 30% iterations can be reduced.
Deep neural networks based on SRGAN single image super-resolution reconstruction can generate more realistic images than CNN-based super-resolution deep neural networks. However, when the network is deeper and more complex, unpleasant artifacts can result. Through a lot of experiments, we can use the ESRGAN model to avoid such problems. When using the ESRGAN model for super-resolution reconstruction, the perceived index of the resulting results does not reach a lower value. There are two reasons for this: (1)ESRGAN does not expand the feature maping. ESRGAN uses 128*128 to obtain the feature information of the image by default, and can't get more image information better. (2) ESRGAN did not re-optimize the generated image. Therefore, we propose ESRGAN-Pro to optimize ESRGAN for the above two aspects, combined with a large amount of training data, and get a better perception index and texture.
The registration of infrared and visible images is a common multi-modal image registration, which is widely used in military, remote sensing and other fields. After describing the registration of infrared and visible images, this paper mainly introduces the SIFT(Scale Invariant Feature Transform) algorithm and SURF(Speeded Up Robust Features) algorithm based on local invariant feature in image registration. First, we extract SIFT and SURF key points of infrared and visible images respectively. Next, we use approximation nearest neighbor search method based on k-d tree algorithm to match key points. Finally, in order to improve the matching accuracy, the RANSAC algorithm is used to eliminate the error matching points. The experiment shows that for these two algorithms, the number of key points in infrared image is obviously smaller than that of visible light image. For these two images, the SURF algorithm is better than the SIFT algorithm.
In order to carry on the gait recognition fast and effectively, a novel gait recognition based on (2D)2 PCA and HMM is proposed in this paper . Firstly, establish a stable background model by using the adaptive background modeling and get the goal of human motion by using background subtraction. As for the existence of the shadow of the human body and inanity, this article makes shadow detection and elimination by using color space conversion respectively and handles human target image soothingly by using regional filling and morphological filtering on smoothing. the number of high-dimensional video images is high, uses the (2D)2PCA features extracted to reduce the dimensions so as to solve the curse of dimensionality, makes use of HMM to classification training of Gait features extracted, then the classification results are analyzed. This gait recognition system is achieved loading OpenCV under VC++6.0 visual library. Our experimental results demonstrate that the method is effective and has achieved a good recognition effect on CASIA gait database including three different multi-views.
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