Yingying Zhu

A solution of dynamic VMs placement problem for energy consumption optimization based on evolutionary game theory

The computational model of energy consumption is built to serve as an evaluation function.An algorithm based on evolutionary game theory is proposed to solve the problem of dynamic VMs placement.It is analyzed that the algorithm can theoretically reach the optimal solution of the dynamic VMs placement problem.The algorithm can take the initial mapping into account and generate an executable list of VMs live migrations from the initial state to the target state. Power saving of data centers has become an urgent problem in recent years. For a virtualized data center, optimizing the placement of virtual machines (VMs) dynamically is one of the most effective methods for power savings. Based on a deep study on VMs placement, a solution is proposed and described in this paper to solve the problem of dynamic placement of VMs toward optimization of their energy consumptions. A computational model of energy consumption is proposed and built. A novel algorithm based on evolutionary game theory is also presented, which successfully addresses the challenges faced by dynamic placement of VMs. It is proved that the proposed algorithm can reach the optimal solutions theoretically. Experimental results also demonstrate that, by adjusting VMs placement dynamically, the energy consumption can be reduced correspondingly. In comparison with the existing state of the arts, our proposed method outperforms other five algorithms tested and achieves savings of 30-40% on energy consumption.

A lifecycle model for simulating bacterial evolution

This paper presents a lifecycle model (LCM) to simulate bacterial evolution from a finite population of Escherichia coli (E. coli) bacteria. The potential of this approach is in relating the microscopic behaviors of single bacterial cell to the macroscopic effects of bacterial colonies. This can be accomplished via use of an individual-based modeling method under the framework of agent-environment-rule (AER). Here, our study focuses on investigating the behaviors at different developmental stages in E. coli lifecycle and developing a new biologically inspired methodology for static or dynamic systems. The experimental results through a varying environment demonstrates that our model can be used to study under which circumstances a certain bacterial behaviors emerges, and also give an inspiration to design a new biological optimization algorithm being used for optimization problems.

Large-scale video copy retrieval with temporal-concentration SIFT

The scale-invariant feature transform (SIFT) feature plays a very important role in multimedia content analysis, such as near-duplicate image and video retrieval. However, the storage and query costs of SIFT become unbearable for large-scale databases. In this paper, SIFT features are robustly encoded with temporal information by tracking the SIFT to generate temporal-concentration SIFT (TCSIFT), which highly compresses the quantity of local features to reduce visual redundancy, and keeps the advantages of SIFT as much as possible at the same time. On the basis of TCSIFT, a novel framework for large-scale video copy retrieval is proposed in which the processes of retrieval and validation are implemented at the feature and frame level. Experimental results for two different datasets, i.e., CC_WEB_VIDEO and TRECVID, demonstrate that our method can yield comparable accuracy, compact storage size, and more efficient execution time, as well as adapt to various video transformations.

Haze removal method for natural restoration of images with sky

Abstract Most haze removal methods fail to restore long-shot images naturally, especially for the sky region. To solve this problem, we proposed a Fusion of Luminance and Dark Channel Prior (F-LDCP) method to effectively restore long-shot images with sky. The transmission values estimated based on a luminance model and dark channel prior model are fused together based on a soft segmentation. The transmission estimated from the luminance model mainly contributes to the sky region, while that from the dark channel prior for the foreground region. The airlight also is adjusted to adapt to real light by sky region detection. A user study and objective assessment comparison with a variety of methods on long-shot haze images demonstrate that our method retains visual truth and removes the haze effectively.

Automatic Audio Genre Classification Based on Support Vector Machine

Audio classification is very important in audio indexing, analysis and content-based video retrieval. In this paper, we have proposed a clip-based support vector machine (SVM) approach to classify audio signals into six classes, which are pure speech, music, silence, environmental sound, speech with music and speech with environmental sound. The classification results are then used to partition a video into homogeneous audio segments, which is used to analyze and retrieve its high-level content. The experimental results show that the proposed system not only improves classification accuracy, but also performs better than the other classification systems using the decision tree (DT), K nearest neighbor (K-NN) and neural network (NN).

Data Matrix Code Location Based on Finder Pattern Detection and Bar Code Border Fitting

The 2-D bar code possesses large capacity of data, strong ability for error correction, and high safety, which boosts the 2-D bar code recognition technology being widely used and developed fast. This paper presents a novel algorithm for locating data matrix code based on finder pattern detection and bar code border fitting. The proposed method mainly involves three stages. It first extracts candidate regions that may contain a data matrix code by morphological processing and then locates the data matrix code roughly by detecting “L” finder pattern and the dashed border on the candidate regions. Finally, the lines fitted with the border points are used as the borders of data matrix code. A number of data matrix code images with complexity background are selected for evaluations. Experimental results show that the proposed algorithm exhibits better performance under complex background and other undesirable conditions.

Detection and Recognition of Abnormal Running Behavior in Surveillance Video

Abnormal running behavior frequently happen in robbery cases and other criminal cases. In order to identity these abnormal behaviors a method to detect and recognize abnormal running behavior, is presented based on spatiotemporal parameters. Meanwhile, to obtain more accurate spatiotemporal parameters and improve the real-time performance of the algorithm, a multitarget tracking algorithm, based on the intersection area among the minimum enclosing rectangle of the moving objects, is presented. The algorithm can judge and exclude effectively the intersection of multitarget and the interference, which makes the tracking algorithm more accurate and of better robustness. Experimental results show that the combination of these two algorithms can detect and recognize effectively the abnormal running behavior in surveillance videos.

Video scene classification and segmentation based on Support Vector Machine

Video scene classification and segmentation are fundamental steps for multimedia retrieval, indexing and browsing. In this paper, a robust scene classification and segmentation approach based on support vector machine (SVM) is presented, which extracts both audio and visual features and analyzes their inter-relations to identify and classify video scenes. Our system works on content from a diverse range of genres by allowing sets of features to be combined and compared automatically without the use of thresholds. With the temporal behaviors of different scene classes, SVM classifier can effectively classify presegmented video clips into one of the predefined scene classes. After identifying scene classes, the scene change boundary can be easily detected The experimental results show that the proposed system not only improves precision and recall, but also performs better than the other classification systems using the decision tree (DT), K nearest neighbor (K-NN) and neural network (NN).

Adaptive Dehaze Method for Aerial Image Processing

Remote sensing images or images collected by unmanned aerial vehicles in the hazy weather are easily interfered by scattering effect generated by atmospheric particulate matter. The terrible interference will not only lead to the images quality seriously degraded, but also result in a bad effect on the process of images feature extraction and images feature matching. In this paper, by proposing an effective adaptive dehaze method, we compare the statistical results of feature detection and matching based on Scale-invariant feature transform (SIFT) detector and descriptor before and after haze removal. And we also provide the comparisons of image stitching task. The experimental results show that, after the haze removal is implemented on hazy images, more SIFT feature keypoints and SIFT matching keypoints will be extracted, which is also beneficial to images stitching. Moreover, the proposed adaptive method performs better than the original dehaze method.

A Temporal-Compress and Shorter SIFT Research on Web Videos

The large-scale video data on the web contain a lot of semantics, which are an important part of semantic web. Video descriptors can usually represent somewhat the semantics. Thus, they play a very important role in web multimedia content analysis, such as Scale-invariant feature transform SIFT feature. In this paper, we proposed a new video descriptor, called a temporal-compress and shorter SIFTTC-S-SIFT which can efficiently and effectively represent the semantics of web videos. By omitting the least discriminability orientation in three stages of standard SIFT on every representative frame, the dimensions of the shorter SIFT are reduced from 128-dimension to 96-dimension to save space storage. Then, the SIFT can be compressed by tracing SIFT features on video temporal domain, which highly compress the quantity of local features to reduce visual redundancy, and keep basically the robustness and discrimination. Experimental results show our method can yield comparable accuracy and compact storage size.