• Poonam Gohad
• V.S. Narayana Tinnaluri

Due to the wide spread use of computer technology, image processing techniques have become largely important. In designing of systems, very often for the interpretation or manipulation of image data, there is need to perform an operation on image, called segmentation, where the image is partitioned on the basis of various parameters. Segmentation finds the boundaries, objects as well as other relevant data from an image. From the segmented image features can be extracted which are color, texture, intensity. The two Broad categories of segmentation techniques are edge-based and region-based. The techniques involved in above mentioned categories are: K-means, Fuzzy C-means, thresholding which consists of global thresholding and variable thresholding, KNN, greedy algorithm etc. Soft computing approaches like fuzzy logic, genetic algorithm and neural network are also used in segmentation of various images. Classification techniques are used for categorizing the target or labeling them which involves techniques like K-Nearest Neighbor, Support Vector Machine. This paper consists of a study of the various image segmentation and classification techniques.

Thresholding, greedy algorithm, k-means, Fuzzy C-means, Neural network.

"A Study on Segmentation and Classification Techniques in Image Processing.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.514-516, April-2019, Available :http://www.jetir.org/papers/JETIR1904789.pdf

"A Study on Segmentation and Classification Techniques in Image Processing.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp514-516, April-2019, Available at : http://www.jetir.org/papers/JETIR1904789.pdf