Codreanu, Valeriu

Karaaba, Mahir

Okafor, Emmanuel

Pawara, Pornntiwa

Schomaker, Lambertus

Surinta, Olarik

Wiering, Marco

English

Most research in image classification has focused on applications such as face, object, scene and character recognition. This paper examines a comparative study between deepconvolutional neural networks (CNNs) and bag of visual words (BOW) variants for recognizing animals. We developed two variants of the bag of visual words (BOW and HOG-BOW) and examine the use of gray and color information as well as different spatial pooling approaches. We combined the final feature vectors extracted from these BOW variants with regularized L2 support vector machine (L2-SVM) to distinguishbetween classes within our datasets. We modified existing deep CNN architectures: AlexNet and GoogleNet, by reducing thenumber of neurons in each layer of the fully connected layers and last inception layer for both scratch and pre-trained versions.Finally, we compared the existing CNN methods, our modified CNN architectures and the proposed BOW variants on our novelwild-animal dataset (Wild-Anim). The results show that the CNN methods significantly outperform the BOW techniques

Proceedings - University of Groningen

Comparative Study Between Deep Learning and Bag of Visual Words for Wild-Animal Recognition

https://pure.rug.nl/ws/files/37861337/SSCI16_paper_255.pdf

Comparative Study Between Deep Learning and Bag of Visual Words for Wild-Animal Recognition

Abstract

Similar works

Full text

Available Versions

Proceedings - University of Groningen