Terrain Features Extraction Using Natural Computing Techniques Er. Harneet Kaur1, M.Tech Student Department of Computer Science & Engineering, RIEIT, Railmajra, Ropar, India er.harneetkaur@gmail.com Er. Gopika Kakkar2 M.Tech Student Department of Computer Science & Engineering, RIEIT, Railmajra, Ropar, India gopikakakkar@gmail.com ABSTRACT In this paper, we study the feature extraction methods for Image Classification discovered till date.Image Classification methods have been significantly developed in the last ten years. A novel algorithm based on analysis ofBacterial Foraging Optimization (BFO), Biogeography Based Optimization (BBO), Fuzzy Logic, Genetic Algorithm (GA), and Artificial Bee Colony (ABC) are proposed to classify an image. This study applies an experience-based approaches related to natural computing in the field of image classification. The main advantage of this set of algorithm over other experience-based techniques is that its search space is vast in nature. Keywords: Image Classification, Computing, Optimization, Intelligence. Natural Natural 1.0 INTRODUCTION Usually (in the past) classification of images--at least in the area of computer vision--has been considered using mostly static representations. Natural visual input, however, consists of spatial-time-related patterns and psychophysical studies back up (with proof) that the human visual system can fully use (for profit) built-in time-related (features/ qualities/ traits) [11]. The term image classification refers to the labelling of images into one of some predefined categories. The purpose of the classification process is to separate and label all pixels in a digital image into one of (more than two, but not a lot of) land cover classes, or "themes". This separated and labelled data may then be used to produce maps of the land cover present in an image. Normally, multispectral data are used to perform the classification and, in fact, the pattern present within the data for each pixel is used as the number-based basis for separation and labelling (Lillesand and Kiefer, 1994). The goal of image classification is to identify and show/represent, as a (like nothing else in the world) grey level (or color), the features happening in an image in terms of the object or type of land cover these features actually represent on the ground. Er. Harish Kundra3 Head of Department Science & Engineering, RIEIT, Railmajra, Ropar, India hodcseit@rayatbahra.com If one considers classification of image sequences a possible data representation consists of individual frames, (in other words) the simplest view-based representation containing only raw pixel data. This study present a second representation which takes energetic/changing information into account. This is done by extraction of interest points in the image (in our case, corners) and construction of visual features by using the corner positions together with their local pixel neighborhoods. A set of important features is then found by watching and following these visual features over the input sequence. This representation can be seen as incorporating the (in the mind as an opinion or judgment before doing something, seeing something, meeting someone, etc.) knowledge of an (one after the other) image presentation (time-related (uninterrupted, constant quality)). Classification between the object is easy task for machines. The raise of high capacity computers, the availability of high quality and low price- video cameras, and increase the need of automatic video analysis has generated an interest in object classification algorithm. A simple classification system consists of camera fixed high above the interested zone, where image are captured & consequently processed. Classification includes image sensors, image pre-processing, object detection, segmentation & classification or feature extraction. Classification system consists of database that contains predefined patterns that compares with detected object to classify in proper category. Image classification is an important and challenging task in various application domains; include bio-medical imaging, biometry, videosurveillance, vehicle navigation, industrial visual inspection, robot navigation and remote sensing [1]. The two main methods for image classification are supervised and unsupervised classification. Supervised classification requires prior information before testing process and it must collected by analyst. In this analyst identifies representative training sites for each informational class and also here algorithm generates decision boundaries. Commonly used supervised classification approaches are parallelepiped, minimum distance to mean and maximum likelihood. In unsupervised classification, prior information is not needed. It does not require human annotation, it is fully automated. This algorithm identifies clusters in data and also analyst labels clusters [2]. paradigms’ which denotes that natural intelligence can be computed under umbrella of computational paradigms[3] Natural computing can be defined as a means of using nature to resolve abstruse problems whose features are as follows: Flexible: related to diverse problems. Adaptive: Problem that can be related with dynamic atmospheres through self-adaptation. Decentralized: deprived of a central consultant. Autonomous: can perform devoid of human intrusion. Optimization is a practical science which discovers the finest values of the factors of a problem that may take under quantified circumstances. It is usually meets mathematical problem in all engineering restraints. It precisely means discovering the optimum solution. The stochastic algorithms are of two types: heuristic and meta-heuristic. Heuristic denotes ‘to find’ or ‘to discover by test and error’. For instance, eminence results can be achieved in rational amount of time but it cannot be certain that optimum elucidations are achieved. Additionally, fortification over heuristic algorithms is known as meta-heuristic algorithms. Meta means ‘beyond’ or ‘advanced level. 2.0 LITERATURE SURVEY 2.0.1 Image Classification Based on Fuzzy logic Nedeljkovic et al 2013: [4] Fig 1.0: Flow chart depicting Natural Computing Methodology Natural Computing is the field of research that deals with computational techniques that deal with natural inspiration. It attempts to understand the world around us in terms of information processing. It’s a highly interdisciplinary field that connects the natural science with computing science; both at the level of information technology and at the level of fundamental research. Its strength lies in its flexibility to create models that suit the needs arising in applications (context of discovery, model generation). In addition, it emphasize the need of intuitive and interpretable models, which are tolerant to the imprecision and uncertainty. Natural intelligence computational paradigms (NICP) is a incorporation of two stipulations ‘natural intelligence’ and ‘computational Fuzzy logic relatively a young theory. The major advantage of this theory is that it allows the natural description in terms of linguistic, problems that should be solved rather than in terms of relationship between precise numerical values. This advantage deals with complicated system in simple way, this is the reason why fuzzy logic theory is widely applied in technique. It is also possible to classify the remotely sensed image, in such a way the certain land cover classes are clearly represented in the resulting image. So we can use fuzzy logic techniques to diminish the influence of person dealing with supervised classification. In this paper, a prior knowledge about spectral information for certain land cover classed is used in order to classify spot image in fuzzy logic manner. The later was done with mat-lab fuzzy logic toolbox. Some information needed for membership function definition was taken from supervised maximum likelihood classification. The idea for result comparison came from the image works used for supervised procedure. The result of two procedures based on both pixel by pixel techniques were com-paired, encouraging and conclusion remark came out. 2.0.2 Image Classification Neuro-fuzzy Approach using S. Panigrahi et al 2013: [5] Image classification is based on two different approachs namely, Artifical neural network and neurofuzzy system. It is seen that neurofuzzy system is better classification technique than ANN.The design used the discreate cosin than fron (DCT) for feature Extraction and Artifical neural networks and neurofuzzy system for classification.As DCT works on gray level image,the colour image is transformed into gray levels. A neuro-fuzzy approach was used to take advantage of neural networks ability to learn, membership degrees and functions fuzzy logic. This study proves that neuro-fuzzy model performed better than neural in classification of image of two types. 2.0.3 Biogeography based Satellite Image Classification H. Kundra et al 2009: [6] Biogeography scrutinizes the geographical dispersal of biological creatures. The approach of the researchers is toacquire knowledge from nature. Biogeography Based Optimization is a flourishing nature inspired technique to obtain the optimum solution of the problem. Satellite image classification is an imperative task asit is the solitarytechnique to recognizearound the land cover map of remote areas. Though satellite images have been classified over years using different techniques, the scholars are constantlydiscoveringadditional strategies for satellite image classification inorder to choose the most suitable technique for the feature extraction task in hand. This studyfocuses on classification of the satellite image of a particular land cover using the concept of Biogeography based Optimization. 2.0.4 Genetic clustering for automatic evolution of clusters and application to image classification S. Bandyopadhyayet al 2002: [7] presents the searching competence of genetic algorithms has been demoralizedfor inevitablydevelopingthe number of clusters as well as suitableclustering of any data set. A new string illustration, including both real numbers and the do not care symbol, is used to encrypt a variable number of clusters. The Davies–Bouldin index is used togaugethe validity of the clusters. Efficiency of the genetic clustering scheme is explained for both artificial and real-life data sets. The projectedtechnique is able to discriminate some characteristic landcover types in the image. 2.0.5 PBBO: A New Hybrid algorithm for Satellite Image Classification H. Kundra et al 2012: [8] Many optimization techniques have been evolved PSO and BBO are two techniques that have been widely used in swarm optimization.PSO is better than many genetic algorithms.PSO has applications in Various area like optimization,Netural networks training,fuzzy controls etc. BBO is based on science of biogeography. 2.0.6 A hybrid FPAB/BBO Algorithm for Satellite Image Classification H. Kundra et al 2010: [9] recently, remote sensing has been utilized for the classification of satellite image on a very large scale. This application employs image classification using swarm computing technique. This study presents a novel swarm data clustering approachgrounded upon flower pollination by artificial bees to gather the satellite image pixels. The goal of collecting is to split a set of data points into self-alike groups. Those groups will be additionally classified using Biogeography Based Optimization. The outcomes indicate that highly precise classification of the satellite image is attained by using the projeced algorithm. 2.0.7 Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting A. Chakrabarty et al 2012: [10] the study refer to the progression of a hyperspectral image classification structure using support vector machines (SVM) with abnormally weighted kernels. The kernels are fabricated during the directing phase of the SVM using optimum spectral weights predictable using the Bacterial Foraging Optimization (BFO) algorithm, a popular stateof-the-art stochastic optimization algorithm. The augmented kernel tasks are then in the SVM pattern for bi-classification of pixels in hyperspectral images. Conclusively, the use of the BFO-based technique is endorseddue to its superior functioning, in contrast to other modern stochastic bio-inspired algorithms. 2.0.8 Satellite Image Classification by Hybridization of FPAB Algorithm and Bacterial Chemotaxis P. Singh et al 2011: [11] Bacterial Foraging Optimization (BFO) has been extensively accepted as a universal optimization technique. This method is projected by K.M. Passino in 2002 to solve complex problems of the real world. This study aims to categorize the satellite image using the theory of Bacterial Foraging Optimization. One importantphase in BFO is the calculationof Chemotaxis, where a bacterium takes stages over the foraging landscape in order to attain regions with high nutrient content. This new-fangledtechniqueexhibits an enhanced highly accurate results for the classification of satellite image when the projected algorithm is used. 2.0.9 Extended Biogeography Based Optimization for Natural Terrain Feature Classification from Satellite Remote Sensing Images V. K. Panchal et al 2011: [12] Remote sensing image classification in latest years has been a thriving area of universalstudy for procuring geo-spatial information from satellite data. In Biogeography Based Optimization (BBO), factsdistribution between candidate problem solutions or habitats depends on the migration process of the ecosystem. The approvedmethodology calculates the migration rate using Rank- based fitness standards. The calculations indicate that highly accurate land-cover features are obtained using the protracted BBO technique. 2.0.10 Fusion of Biogeography based optimization andArtificial bee colony for identification of NaturalTerrain Features H. Kundra et al 2012: [13] Swarm Intelligence methods accelerate the pattern and collimation of the notablecapability of group associates to aim and learn in abackground of eventuality and corrigendum from their nobles by sharing evidence. This researchpresents aninnovativemethod of combination of two intelligent techniques generally to enhance the performance of a single intelligent technique by means of information contribution. Biogeography-based optimization (BBO) is currently acquiredempirical algorithm, which develops to be a strong contestant in swarm intelligence with the inspiring and steady performance. However, as BBO deprivationsinherent property of clustering, its behavior can be substituted with the honey bees of artificial bee colony (ABC), a new swarm intelligent technique. These two approaches can be clubbed to create a new approach which is simple to implement and gives more optimum results than the results when BBO is used. 3.0 CONCLUSION The above study depicts that the natural computing techniques namely, BBO, Fuzzy, ABC, Hybrid ABC/BBO and GA plays a vital role in image classification. The study shows Fuzzy and ABC technique results in more efficient image classification whereas Genetic Algorithm(GA) is second reliable. Furthermore, reliability decreases from hybrid ABC/BBO to the least reliable being BBO technique. 1 4.0 FUTURE SCOPE Natural Computing is anxious with this type of computing in addition with its main advantage for informatics, viz., human-designed computing stimulated by nature. Study in natural computing is sincerely interdisciplinary, and therefore natural computing forms a link between informatics and natural sciences. It has already subsidized immensely to human-designed computing: consider, e.g., all the developments made through neural networks, evolutionary algorithms, quantum computing and molecular computing. Essentially, this investigation has headed already to a profounder and wider empathetic of the nature of computation. In the impending years the expansion and the presentation of new influential Natural Computing data dispensation tools will become all the more vital, given the fast emergent volume of offered biological data. REFERENCES [1] Pooja kamavisdar, sonam saluja and sonu agarwal “A Survey on Image Classification Approach & Techniques” International journal of advanced research in computer and communication engineering vol2, issue1, January 2013. [2] Jipsa Kurian, V.Karunakaran “A Survey on Image Classification Methods” International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 1, Issue 4, October 2012, ISSN: 2278 – 909X. 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