IMAGE SEGMENTATION AND CLASSIFICATION

Image segmentation is the fundamental step for analysing the images and extracting data from them. It is already a widely researched field but it still provides the various challenges to the researches. This paper concentrates on basic methods which is used for image segmentation and classification. Image segmentation can be categorized as semiinteractive approach and fully automatic approach and the algorithms are developed either of these approaches. Image segmentation is like a step and it helps to influences the overall success to understand the image. This is the interesting application for testing the robustness of our approaches due to the complexity of the images and consequently we believe the algorithms will be suitable for many other real world applications. Introduction: Digital image processing is the application of various algorithms on the image to improve the quality of the image and to avoid the noise and unwanted pixels and also to obtain more information about the image. By using the various image processing techniques image segmentation is the crucial step to analyse the image. This paper mainly focuses on various methods and followed by various algorithms for image segmentation. Here we compare the various methods for image segmentation by taking test images. The images are operated by Mat Lab software. An image is basically a two dimensional function of coordinates f(x,y) and the amplitude of the image will gives the intensity value of the image. The image can be expressed as a product of function of illumination and reflection. f(x,y)=i(x,y). r(x,y) Where i(x,y) is the function of intensity and r(x,y) is the function of reflectivity. Our proposed framework is well suited for the applications and where the final outcome is accurate segmentation and classification of object in an image. The user have much interest to find the images by using this method within a large collections of objects and shape is critical to its recognition. E.Vijayan* et al. International Journal of Pharmacy & Technology IJPT| Sep-2016 | Vol. 8 | Issue No.3 | 18325-18334 Page 18326 Image segmentation: Image segmentation refers to the process of dividing the images into disjoint regions. This process is fundamental on computer vision in many applications such as image retrieval, visual summary and image based modelling. Image segmentation is the midlevel processing technique is used to analyse the image and this can be defined as a processing technique and it is used to classify or cluster the image into several disjoint parts by grouping the pixels to form the region of homogeneity based on the pixel characteristics like gray level, colour, texture , intensity and other features. The main purpose of the segmentation process is to get the more information about the image region which helps the annotation of the object scene. Image segmentation is the domain-partition of the image into a set of visually distinct and homogenous regions with respect to certain properties. The main objective of the segmentation process is to differentiate the object and background of an image. Proposed approach: There are few stages for image segmentation and classification they are 1) image segmentation and 2) classification and 3) feature extraction. A. Preliminary segmentation: Preliminary segmentation serves has two functions: 1) removes a large number of pixels from the image prior to assign the labels and 2) reduce the large number of blogs which it is processed. In many applications it can be implemented as a binary region mask and defining the object of interest. The preliminary segmentation has perform through the correlation based background subtraction. Preliminary segmentation results for adults and infant images. (a)adult image (b)infant image (c)preliminary segmentation for adult image (d)preliminary segmentation for infant image. B. Classification: The purpose of region labelling is to group together regions with common attributes for example grayscale, texture etc. There are many mechanisms is proposed for defining the common characteristics such as expectation mechanism (EM) , normalize cuts, relaxation methods and region growing methods. E.Vijayan* et al. International Journal of Pharmacy & Technology IJPT| Sep-2016 | Vol. 8 | Issue No.3 | 18325-18334 Page 18327 Region labelling for instant image. (a) EM labelling, (b)result for mode filtering, (c)result for small region removal. C. Feature extraction: Feature Extraction has two methods: 1)they define a classification problem using tree or graph distance and 2)they build the segmentation specific identical sub regions in the images. The two methods have each of these descriptions moments for region based descriptions and Fourier descriptors for boundary based descriptions. The combinations of these speed up mechanism will boost the classification processing for each moment calculated.