For a long time, video copyright infringement problems have emerged one after another, and the detection of video similarity is an important approach of identifying video copyright infringement. Concerning the problems of the correlation difficulty of multi-feature relation and high time complexity in the existing video similarity detection methods, a fast comparison method based on perceptual hashing and dicing was proposed. First, the key image frames of the video were used to generate a digital fingerprint set. Then, based on the dicing method, the corresponding inverted index was generated to speed up the comparison between digital fingerprints. Finally, the similarity was judged according to the obtained Hamming distance between the digital fingerprints. Experimental results show that the proposed method can reduce the detection time by an average of 93% with ensuring the detection accuracy compared to the traditional perceptual hashing comparison methods; in the comparison with three common methods including Multi-Feature Hashing (MFH), Self-Taught Hashing (STH) and SPectral Hashing (SPH), the mean Average Precision (mAP) of the proposed method is increased by 1.4%, 2% and 2.3%,respectively, and the detection time is shortened by 25%, 32% and 16%, respectively, which verifies the feasibility of the proposed method.

Smooth texture details are easily lost in the process of image texture enhancement. Although fractional-order differential enhancement can preserve the texture details of smooth regions nonlinearly, it is sensitive to frequency resolution. Focusing on this problem, a fractional differential texture enhancement algorithm based on wavelet transform was proposed and applied to texture enhancement of liver tumor regions in plain Computed Tomography (CT) images. Firstly, wavelet transform was used to decompose the image region of interest into multiple subband components. Then, a fractional differential mask with compensation parameter was constructed based on fractional-order differential definition. Finally, the mask was used to convolve with each high frequency subband component respectively, and the image region of interest was recombined by using reverse wavelet transform. The experimental results show that the algorithm effectively preserves the low-frequency smooth texture details while observably enhances the high-frequency contour information of the tumor region by a relatively large fractional order:compared with the original region, the enhanced hepatocellular carcinoma region has the information entropy increased by 36.56% averagely, the average gradient increased by 321.56% averagely, and the mean absolute difference of 9.287 averagely; compared with the original region, the enhanced hepatic hemangioma region has the information entropy increased by 48.77% averagely, the average gradient increased by 511.26% averagely, and the mean absolute difference of 14.097 averagely.

The image segmentation method based on appearance-model has high computational complexity in iterative positioning feature points, and it is difficult to optimize the nonlinear local feature. To solve these above problems and locate feature points of left ventricular endocardium and epicardium, a gradient decent algorithm based on supervised learning was proposed, a multi-resolution pyramid model of 4 levels was built, and a new feature extraction function based on Bhattacharyya coefficient, namely B-SIFT, was used to replace the Scale Invariant Feature Transform (SIFT) feature in the original method. Firstly, the training set images were normalized to unify the size of each TransEsophageal Echocardiography (TEE). Then the supervised descent model based on B-SIFT and multi-resolution pyramid was built to get a gradient descent direction sequence that approaches the actual values. Finally, the learned direction sequence was applied to the test set to obtain the segmentation results of left ventricular. The experimental results show that compared with the traditional gradient decent method based on supervised learning, the average segmentation error of the proposed method is reduced by 47%, and the iteration results are more closer to the actual values compared with the single-scale method.

Feature extraction is a key step of image retrieval and image registration, but the single feature can not express the information of medical images efficiently. To overcome this shortcoming, a new algorithm for medical image retrieval combining global features with local features was proposed based on the characteristics of medical images. First, after studying the medical image retrieving techniques with single feature, a new retrieval method was proposed by considering global feature and relevance feedback. Then to optimize the Scale-Invariant Feature Transform (SIFT) features, an improved SIFT features extraction and matching algorithm was proposed. Finally, in order to ensure the accuracy of the results and improve the retrieval result, local features were used for stepwise refinement. The experimental results on general Digital Radiography (DR) images prove the effectiveness of the proposed algorithm.

This paper proposed a liner programming model to deal with the Quay Crane (QC) allocation and scheduling problem for single ship under the circumstance of fixed berth allocation. With the aim of minimizing the working time of the ship at berth, the model considered not only the disruptive waiting time when the quay cranes were working, but also the workload balance between the cranes. And an Improved Ant Colony Optimization (IACO) algorithm with the embedding of a solution space split strategy was presented to solve the model. The experimental results show that the proper allocation and scheduling of quay cranes from the model in this paper can averagely save 31.86% of the crane resource compared with full application of all available cranes. When comparing to the solution solved by Lingo, the results from IACO algorithm have an average deviation of 5.23%, while the average CPU (Central Processing Unit) computational time is reduced by 78.7%, which shows the feasibility and validity of the proposed model and the algorithm.

Most existing cloud storage systems are based on the model, which leads to a full dataset scan for multi-dimensional queries and low query efficiency. A KD-tree and R-tree based multi-dimensional cloud data index named KD-R index was proposed. KD-R index adopted two-layer architecture: a KD-tree based global index was built in the global server and R-tree based local indexes were built in local server. A cost model was used to adaptively select appropriate R-tree nodes to publish into global KD-tree index. The experimental results show that, compared with R-tree based global index, KD-R index is efficient for multi-dimensional range queries, and it has high availability in the case of server failure.

In the recent years, the computer image understanding has wide and profound applications in intelligence traffic, satellite remote sensing, machine vision, image analysis of medical treatment, Internet image search and etc. As its extension, the image holistic scene understanding is more complex and integrated than basic image scene understanding task. In this paper, the basic framework for image understanding, the researching implication and value, typical models for image holistic scene understanding were summarized. The four typical holistic scene understanding models were introduced, and the model frameworks were thoroughly compared. At last, some research insufficiency and future direction in image holistic scene understanding were presented, which pointed out some new insights for the further research in this area.

Energy efficiency is an important metric in wireless Ad Hoc networks. Until now, there is no universally accepted definition of energy efficiency, and the related results are asymptotic or qualitative, which has limited its applicability. By regarding a bit as a physical particle with one unit of mass, the authors assumed that a bit in transmission possessed a certain amount of kinetic information energy. As a result, the energy efficiency of wireless transmission was defined as the ratio of information energy to physical energy. A quantitative analysis on energy efficiency in wireless transmission was carried out and meaningful results were obtained. It is concluded that the energy efficiency changes non-monotonously with the transmission power, and there is an optimal transmission power, with which the maximum energy efficiency will be acquired. The optimal transmission power was given to help the protocol design. Based on the theoretical results, a practical solution for transmission power control was proposed, and an extensive experimental study of it was given on the CC2420 radio. The results show the effectiveness of the proposed transmission power control scheme.

Mining the hidden knowledge in the spatial data of Geographic Information System (GIS) is an important direction in the study fields of GIS and data mining. The technique of concept lattice is very important in finding association rules. In this paper, an algorithm of building concept lattice based on incremental method was proposed to improve the speed of building lattice by comparing the extent of the concept and introducing support constraint. It could also simplify the lattice and make it easier to mine rules. To expand the application of the algorithm, it was applied in finding association rules of the spatial data in GIS and got practicable application result.

Based on SAML2.0 standard,a scheme of supporting trust transfer of mobile equipment was presented.The architecture and the flow of this system were described. Several problems and relative countermeasures about security and deployment were analyzed and proposed.