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Srinivasagan, K. G.
- An Improved Dynamic Data Replica Selection and Placement in Hybrid Cloud
Authors
1 Department of Computer Science and Engineering-PG, National Engineering College, Kovilpatti, Tamilnadu, IN
Source
Networking and Communication Engineering, Vol 6, No 3 (2014), Pagination: 112-117Abstract
Cloud computing platform is getting more and more attentions as a new trend of data management. Data replication has been widely used to speed up data access in cloud. Replica selection and placement are the major issues in replication. In this paper we propose an approach for dynamic data replication in cloud. A replica management system allows users to create, register and manage replicas and update the replicas if the original datasets are modified. The proposed work concentrates on designing an algorithm for suitable optimal replica selection and placement to increase availability of data in the cloud. The method consists of two main phases file application and replication operation. The first phase contains the replica location and creation by using catalog and index. In second phase is used to find whether there is enough space in the destination to store the requested file or not. Replication aims to increase availability of resources, minimum access cost, shared bandwidth consumption and delay time by replicating data. The proposed systems developed under the Eucalyptus cloud environment. The results of proposed replica selection algorithm achieve better accessibility compared with other methods.Keywords
Hash Key, Replication, Optimal Selection, Eucalyptus, Hybrid Cloud, Catalog, Virtual Synchrony, State Transition.
- Image Object Retrieval and Recognition Based On Descriptive Features Using SVM
Authors
1 Department of Computer Science and Engineering-PG, National Engineering College, Kovilpatti, Tamilnadu, IN
Source
Digital Image Processing, Vol 6, No 3 (2014), Pagination: 166-171Abstract
Content Based Image Retrieval is a most emerging technique which helps us to retrieve the image from the large scale of image database base on the query image. In this paper, an image object retrieval system using the low-level features of image sub regions is proposed. The descriptive features namely color, shape and texture of the images are computed from the histograms of the quantized color space using color coherence vector, Gabor Filter and Gray Level Co-occurrence Matrix respectively. The N-class SVM is used to classify and labeling the images. Similarity metric between the query and target image is calculated using Euclidean distance measure. Experimental results show that the proposed method provides better retrieving result than existing methods.Keywords
Content Based Image Retrieval, Color Coherence Vector, Gabor Filter, Gray Level Co-Occurrence Matrix, Support Vector Machine.- An Efficient Crowd Behavior Recognition using Motion Patterns for Intelligent Video Surveillance
Authors
1 National Engineering College, Kovilpatti, IN
Source
Biometrics and Bioinformatics, Vol 7, No 1 (2015), Pagination: 23-27Abstract
An automated visual monitoring process expands from low level analysis of object detection and tracking to the interpretation of their behaviors. Analyzing human crowd is an emerging trend in intelligent video surveillance for the purpose of detecting abnormalities. Tracking every human being in a crowd and analyzing their behavior is a challenging task due to occlusions. Hence, the crowd can be handled as a group entity instead of tracking the individual in the crowd. The behavior of the crowd can be distinguished with motion patterns due to prominent spatio-temporal characteristics. The proposed system involves a systematic approach to recognize the global events in human crowd through observing motion patterns such as flow, speed and direction. Initially as a preprocessing step, background subtraction is performed to extract the foreground blobs and optical flow is estimated to obtain the velocity and direction of motion. The human crowds are then clustered based on similar direction and proximity using Adjacency Matrix based Clustering (AMC). After clustering, the centroid and orientation of the cluster are extracted inorder to represent the behavior of crowd. Finally the multiclass Support Vector Machine (SVM) is trained to correctly recognize the behavior of crowd.