Open AccessAnalysis of color feature extraction techniques for Fish Species Identification
Author(s) -
Uéliton Freitas,
Marcio Carneiro Brito Pache,
Wesley Nunes Gonçalves,
Edson Takashi Matsubara,
José Sabino,
Diego André Sant’Ana,
Hemerson Pistori
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2020.13495
Subject(s) - artificial intelligence , computer science , rgb color model , feature extraction , pattern recognition (psychology) , support vector machine , bag of words model in computer vision , histogram , classifier (uml) , computer vision , histogram of oriented gradients , hsl and hsv , feature (linguistics) , contextual image classification , identification (biology) , image (mathematics) , image retrieval , visual word , linguistics , virus , philosophy , virology , biology , botany
Color recognition is an important step for computer vision to be able to recognize objects in the most different environmental conditions. Classifying objects by color using computer vision is a good alternative for different color conditions such as the aquarium. In which it is possible to use resources of a smartphone with real-time image classification applications. This paper presents some experimental results regarding the use of five different feature extraction techniques to the problem of fish species identification. The feature extractors tested are the Bag of Visual Words (BoVW), the Bag of Colors (BoC), the Bag of Features and Colors (BoFC), the Bag of Colored Words (BoCW), and the histograms HSV and RGB color spaces. The experiments were performed using a dataset, which is also a contribution of this work, containing 1120 images from fishes of 28 different species. The feature extractors were tested under three different supervised learning setups based on Decision Trees, K-Nearest Neighbors, and Support Vector Machine. From the attribute extraction techniques described, the best performance was BoC using the Support Vector Machines as a classifier with an FMeasure of 0.90 and AUC of 0.983348 with a dictionary size of 2048.