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Volume 2 - No: 1

Classification of Serranide Species Using Color Based Statistical Features

  • Bilal İşçimen Kırıkhan Vocational School, Mustafa Kemal University, Hatay, Turkey
  • Yakup Kutlu Department of Computer Engineering, Iskenderun Technical University, Hatay, Turkey
  • Cemal Turan Faculty of Marine Sciences and Technology, Iskenderun Technical University, Hatay, Turkey
DOI: 10.28978/nesciences.292352
Keywords: Classification, Serranidae family, HSV, color, texture

Abstract

In this study 6 species of Serranidae family (Epinephelus aeneus, Epinephelus caninus, Epinephelus costae, Epinephelus marginatus, Hyporthodus haifensis, Mycteroperca rubra) were classified by using a color based feature extraction method. A database which consists of 112 fish images was used in this study. In each image, a fish was located on a white background floor with the same position and the images were taken from different distances. A combination of manual processes and automatic algorithms were applied on images until obtaining colored fish sample images with a black background. Since the presented color based feature extraction method avoids including background, these images were processed by using an automatic algorithm in order to obtain a solid texture image from the fish and extract features. The obtained solid texture image was in HSV color space and used due to extract species-specific information from the fish samples. Each of the hue, saturation and value components of the HSV color space was used separately in order to extract 7 statistical features. Hence, totally 21 features were extracted for each fish sample. The extracted features were used within Nearest Neighbor algorithm and 112 fish samples from the 6 species were classified with an overall accuracy achievement of 86%.

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Date

February 2017

Page Number

25-34