Pengenalan Ras Kucing Menggunakan Metode Ekstraksi Ciri Pyramid Histogram Of Oriented Gradients (p-hog)

Atika Ayunda Murwanti, Kurniawan Nur Ramadhani, Prasti Eko Yunanto

Abstract

Abstrak
Kucing merupakan hewan dengan macam ras yang cukup banyak. Dari banyak ras kucing, beberapa
memiliki kemiripan ciri visual yang cukup tinggi seperti ras Siamese, Ragdoll, dan Siamese. Untuk
mempermudah pengenalan kedua ras tersebut, akan dilakukan pengenalan menggunakan metode
ekstraksi ciri Pyramid Histogram of Oriented Gradients (P-HOG) berdasarkan 599 Oxford IIIT Pet Dataset
berupa citra ras kucing Siamese, Ragdoll, dan Birman yang telah dioptimasi. Citra akan melewati tahap
pra-proses untuk mempermudah proses ekstraksi ciri dengan P-HOG. Tahap praproses meliputi pencarian
ROI menggunakan segmentasi grabcut dan proses grayscale. Pengujian yang dilakukan menghasilkan
akurasi tertinggi sebesar 69.2% menggunakan metode P-HOG + SVM dengan kernel linear dan parameter
P-HOG level 3 bin 10.
Kata kunci : Pengenalan ras kucing, histogram of oriented gradients, P-HOG, computer vision
Abstract
Cats are animals with quite a lot of breeds. Of the many cat breeds, some have quite high visual similarities
such as the Siamese, Ragdoll, and Siamese breeds. To facilitate the introduction of the two races, a
recognition introduction will be made using the Pyramid Histogram of Oriented Gradients (P-HOG) feature
extraction method based on optimized 599 Oxford IIIT Pet Datasets in the form of images of Siamese,
Ragdoll, and Siamese cats. The image will go through the preprocessing stage to simplify the feature
extraction process with P-HOG. The preprocessing stage includes ROI search using grabcut segmentation,
and grayscale process. Tests carried out resulted in the best accuracy of 69.2% using the P-HOG + SVM
method with linear kernel and P-HOG parameters on level 3 bin 10.
Keywords: cat breeds recognition, histogram of oriented gradients, P-HOG, computer vision

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