In this paper, a new image compression scheme is proposed. The self similarity of a static image is analyzed in order to implement image compression on basis of similarity and fusion rather than redundancy removal since the former is more similar to visual characteristic of people. Experimental result shows the scheme presented in the paper can has a potential performance on image and video coding.
In the deep learning-based video action recognition, the function of the neural network is to acquire spatial information, motion information, and the associated information of the above two kinds of information over an uneven time span. We propose a network for extracting semantic information of video sequences based on the deep fusion feature of local spatial–temporal information. Convolutional neural networks (CNNs) are used in the network to extract local spatial information and local motion information, respectively. The spatial information is in three-dimensional convolution with the motion information of the corresponding time to obtain local spatial–temporal information at a certain moment. The local spatial–temporal information is then input into the long- and short-time memory (LSTM) to obtain the context relationship of the local spatial–temporal information in the long-time dimension. We add the ability of the regional attention mechanism of video frames in the neural network mechanism for obtaining context. That is, the last layer of convolutional layer spatial information and the first layer of the fully connected layer are, respectively, input into different LSTM networks, and the outputs of the two LSTMs at each time are merged again. This enables a fully connected layer that is rich in categorical information to provide a frame attention mechanism for the spatial information layer. Through the experiments on the three action recognition common experimental datasets UCF101, UCF11, and UCFSports, the spatial–temporal information deep fusion network proposed has a high correct recognition rate in the task of action recognition.
In this paper, firstly, several video shot detection technologies have been discussed. An edited video consists of two kinds of shot boundaries have been known as straight cuts and optical cuts. Experimental result using a variety of videos are presented to demonstrate that moving window detection algorithm and 10-step difference histogram comparison algorithm are effective for detection of both kinds of shot cuts. After shot isolation, methods for shot characterization were investigated. We present a detailed discussion of key-frame extraction and review the visual features, particularly the color feature based on HSV model, of key-frames. Video retrieval methods based on key-frames have been presented at the end of this section. This paper also present an integrated system solution for computer- assisted video parsing and content-based video retrieval. The application software package was programmed on Visual C++ development platform.
A computer-aided detect and diagnosis method based on a human visual attention model,and then use the FOA to initialize the cluster center and fuzzy clustering to complete image ROI extracting are present. The experiment results show the proposed method is effective and valid.
In video action recognition based on deep learning, the design of the neural network is focused on how to acquire effective spatial information and motion information quickly. This paper proposes a kind of deep network that can obtain both spatial information and motion information in video classification. It is called MDFs (the multidimensional motion features of deep feature map net). This method can be used to obtain spatial information and motion information in videos only by importing image frame data into a neural network. MDFs originate from the definition of 3D convolution. Multiple 3D convolution kernels with different information focuses are used to act on depth feature maps so as to obtain effective motion information at both spatial and temporal. On the other hand, we split the 3D convolution at space dimension and time dimension, and the spatial network feature map has reduced the dimensions of the original frame image data, which realizes the mitigation of computing resources of the multichannel grouped 3D convolutional network. In order to realize the region weight differentiation of spatial features, a spatial feature weighted pooling layer based on the spatial-temporal motion information guide is introduced to realize the attention to high recognition information. By means of multilevel LSTM, we realize the fusion between global semantic information acquisition and depth features at different levels so that the fully connected layers with rich classification information can provide frame attention mechanism for the spatial information layer. MDFs need only to act on RGB images. Through experiments on three universal experimental datasets of action recognition, UCF10, UCF11, and HMDB51, it is concluded that the MDF network can achieve an accuracy comparable to two streams (RGB and optical flow) that requires the import of both frame data and optical flow data in video classification tasks.
In this paper, a novel coding scheme of multi-view image is proposed. It is based on fusion rather than redundancy removal since the former is more similar to visual characteristic of people. A multi-view image system using this scheme is designed. BEMD is applied during the fusion and compression process because of its excellent performance on image processing. Experimental results show, without reducing the image quality, the proposed scheme can provide better compression ratio than the previous ones, which open the doors for future applications.
To improve network throughput by using multi-channel properly,proposed distributed multi-channel allocation method based on group management,according to the traffic characteristics of the wireless mesh networks based on 802.11 standards.This method divided the interfaces in node into backhaul interfaces and data transfer interfaces,and could allocate channels to the backhaul interfaces with the least channel interference cost in the interference region.Simulation results show that the proposed method can reduce regional interference,take full advantage of the diversity of channel and get better network throughput.
Clustering in Wireless Sensor Networks(WSNs) is an effective approach for organizing the network into a connected hierarchy.This paper proposes a novel clustering routing algorithm based on Adaptive Particle Swarm Optimization(APSO).Particle Swarm Optimization(PSO) is a typical swarm intelligence algorithm.It's inspired by social behavior of bird flocking and acted as a fine optimization method.This advantage is utilized and improved to gain better convergence.Compared with Low Energy Adaptive Clustering Hierarchy(LEACH) algorithm, this protocol can reduce energy consumption and achieve better network lifetime.
The article research on medical image fusion using multiwavelet transform.According to analysis of different characteristics of CT image and MRI image,we present a new image fusion model based on discrete multiwavelet transform: Source images decomposition using GHM multiwavelet — fuse the multiwavelet coefficients to obtain target image coefficients — carry out multiwavelet reconstruction.We should preprocess the image before decomposition and postprocess the image after reconstruction,which is different from single wavelet. Finally,compare the new fusion method with other classical fusion algorithm to confirm the advantages of the new method.
This paper proposes a compression algorithm based on K-means clustering for high-through DNA sequence (DNAC-K). In DNAC-K, we create cluster of sequences based on K-means clustering method at first, then iterate clusters according to the edit distances of subsequences, and finally, adopt Huffman coding to encode the result of clustering result. Experimental results on several sequencing data sets demonstrate better performance of DNAC-K than many of the current high-throughput DNA sequence compression algorithms.
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