Sanjay Silakari

Image Clustering Using Color and Texture

With the advancement in image capturing device, the image data been generated at high volume. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Due to semantic gap between low-level image features and the richness of human semantics, a challenge with image contents is to extract meaning from the data they contain. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Proposed framework focuses on color and texture as feature. Color Moment and Gabor filter is used to extract features for image dataset. K-Means and Hierarchical clustering algorithm is applied to group the image dataset into various clusters

New Approach for Fast Color Image Encryption Using Chaotic Map

Image encryption using chaotic maps has been established a great way. The study shows that a number of functional architecture has already been proposed that utilize the process of diffusion and confusion. However, permutation and diffusion are considered as two separate stages, both requiring image-scanning to obtain pixel values. If these two stages are mutual, the duplicate scanning effort can be minimized and the encryption can be accelerated. This paper presents a technique which replaces the traditional preprocessing complex system and utilizes the basic operations like confusion, diffusion which provide same or better encryption using cascading of 3D standard and 3D cat map. We generate diffusion template using 3D standard map and rotate image by using vertically and horizontally red and green plane of the input image. We then shuffle the red, green, and blue plane by using 3D Cat map and standard map. Finally the Image is encrypted by performing XOR operation on the shuffled image and diffusion template. Theoretical analyses and computer simulations on the basis of Key space Analysis, statistical analysis, histogram analysis, Information entropy analysis, Correlation Analysis and Differential Analysis confirm that the new algorithm that minimizes the possibility of brute force attack for decryption and very fast for practical image encryption

Survey of Metaheuristic Algorithms for Combinatorial Optimization

paper is intended to give a review of metaheuristic and their application to combinatorial optimization problems. This paper comprises a snapshot of the rapid evolution of metaheuristic concepts, their convergence towards a unified framework and the richness of potential application in combinatorial optimization problems. Over the years, combinatorial optimization problems are gaining awareness of the researchers both in scientific as well as industrial world. This paper aims to present a brief survey of different metaheuristic algorithms for solving the combinatorial optimization problems. Basically we have divided the metaheuristic into three broad categories namely trajectory methods, population based methods and hybrid methods. Trajectory methods are those that deal with a single solution. These include simulated annealing, tabu search, variable neighborhood search and greedy randomized adaptive search procedure. Population based methods deal with a set of solutions. These include genetic algorithm, ant colony optimization and particle swarm optimization. Hybrid methods deal with the hybridization of single point search methods and population based methods. These are further categorized into five different types. Finally we conclude the paper by giving some issues which are needed to develop a well performed metaheuristic algorithm.

An Ethical Way of Image Encryption Using ECC

In the development of 3G devices, all element of multimedia (text image audio and video) are used. To use this information, a channel of high bandwidth and more secured system is required. In this era, network security has become an issue of importance, on which lot of research is going on. We have proposed image encryption method using elliptic curve cryptography (ECC). RSA is too slow compared to ECC because ECC required smaller key size. In this method, every pixel of the original image is transformed into the elliptic curve point (Xm,Ym), these elliptic curve point convert into cipher image pixel. The resulting system gives comparatively small block size, high speed and high security.

Teaching Learning Based Optimization (TLBO) Based Improved Iris Recognition System

Optimization technique plays an major role in the iris system. The optimized feature gives an optimized template for matching process and these optimized template increases the identification rate of iris system. In this paper, we proposed Teaching Learning Based Optimization (TLBO) based iris recognition system in which feature extraction phase of iris recognition system is optimized by using TLBO. The process of feature extraction is performed by texture feature extraction Gabor wavelet transform technique. TLBO is than applied on these features. Teaching learning based optimization algorithm acquired the feature of iris image as a student and generates the optimized feature template as a teacher. The process of optimization designed the fitness constraints function for the selection of feature in student to teacher The proposed algorithm compared with other iris recognition methods Standard Iris Recognition System and Genetic Algorithm optimized Iris Recognition System. Experimental results when applied to CASIA dataset shows superior performance with better recognition rate.

Performance Analysis for Image Encryption Using ECC

Encryption is used to securely transmit data in open networks. Each type of data has its own features, therefore different techniques should be used to protect confidential image data from unauthorized access. In this paper, we introduce a Elliptic Curve Cryptography (ECC) with knapsack for image encryption and analysis the result. The results showed that the less Encryption Time, less power consuming and more reliable compare than Integer factorization problem (RSA). Finally, the security analysis shows that the proposed scheme satisfies the security properties such as mutual authentication, key control, key confirmation, confidentiality of the sensitive image data.

Multi-User FPGA An Efficient Way of Managing Expensive FPGA Resources Using TCP/IP, Wi-Max/ Wi-Fi in a Secure Network Environment

Optimizing the single FPGA board resource as a simulator in a multi user environment regardless of the physical distance improved by TCP/IP. It is important step for IP and system development to use FPGA and IP core to design and improve flow of multi user data in a high speed communication internet environment and optimal utilization of available bandwidth. A user can connect either by Wi-Fi, Wi-Max , USB, PCI or over a network connection may be to another workstation. There is no need for end users to program this functionality at their own ends. In this paper, we have shown that with Xilinxs system generator for, it is possible for multi users to hardware co-simulate IP Cores over any distance via TCP/IP and at any altitude of angle using NLOS Wi-Max, sharing only one FPGA resource. The strategy is based on the time sharing modular approach (TDM), mutually exclusive phenomena. To enhance security of FPGA and IP core co-simulated users we use strong encryption standards. In this paper we have used three encryption standards AES and Camellia and DES. Matlab 7.0 is used ensuring sharing of FPGA board in a multi user, insecure environment to make possible efficient use of FPGA board resource.

Image Segmentation Methods: A Survey Approach

Image Segmentation is one of basic problem and is an important step in image processing to distinguish important object that is of our interest from unnecessary background substrates. So, quality of this step also affects the subsequent steps of recognition. In this paper, we have compiled Edge detection method, Thresholding method, Region based method, and Clustering.

An Exhaustive Survey on Physical Node Capture Attack in WSN

Node capture attack is one of the most dreadful security attack exist in wireless sensor networks. An adversary steals cryptographic key information from a captured node to compromise entire network. So, Security of wireless sensor network is an important issue for maintaining confidentiality and integrity of wireless links. Now-a-days, researchers are paying attention towards developing security schemes against Node capture attack. Our survey provides deep insights of existing modeling techniques of node capture attack in wireless sensor network. It also analyzes various detection and key predistribution schemes for inventing a new scheme to improve resilience against node capture attack..

Integration of Artificial Intelligence and Database Management System: An Inventive Approach for Intelligent Databases

The integration of AI and DBMS technologies promises to play a significant role in shaping the future of computing. AI/DB integration is crucial not only for next generation computing but also for the continued development of DBMS technology. Both DBMS and AI systems represent well established technologies, research and development in the area of AI/DB integration is comparatively new. The motivations driving the integration of these two technologies include the need for (a) access to large amounts of shared data for knowledge processing, (b) efficient management of data as well as knowledge, and (c) intelligent processing of data. In addition to these motivations, the design of Intelligent Database Interface (IDI) was also motivated by the desire to preserve the substantial investment represented by most existing databases. Several general approaches to AI/DB integration and various developments in the field of intelligent databases have been investigated and reported in the paper.