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Assessment of Support Vector Machine for Classification of Sardine Images
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The goal of image classification is to forecast the types of the input image using its features. This research focuses on the classification of sardine fish images via Support Vector Machine. Sardines are abundant in the Pacific and Atlantic Oceans, and are available in all the fish markets around the world. Hence, it is the most common sea food in wide-reaching. The sardine fish has a distinct appearance that sets it apart from other types of fish. So, finding out the best-quality fishes is a task that requires the benefit of classification. The sardine images used for the study are collected from Kanyakumari district, Tamil Nadu, India. Gray-Level Co-Occurrence Matrix (GLCM) is used for the Texture Analysis of the images and to extract the statistical features of images. SVM is applied on data and the categories dates are obtained. Both the algorithms are executed in MATLAB and the experiments are carried out for getting better results.
Keywords
Classification, Image Classification, Image Processing, Support Vector Machine.
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