Abstract—This paper evaluates two deep learning techniques that are basic Convolutional Neural Network (CNN) and AlexNet along with a classical local descriptor that is Bag of Features (BoF) with Speeded-Up Robust Feature (SURF) and Support Vector Machine (SVM) classifier for indoor object recognition. A publicly available dataset, MCIndoor20000, has been used in this experiment that consists of doors, signage, and stairs images of Marshfield Clinic. Experimental results indicate that AlexNet achieves the highest accuracy followed by basic CNN and BoF. Furthermore, the results also show that BoF, a machine learning technique, can also produce a high accuracy performance as basic CNN, a deep learning technique, for image recognition.
Index Terms—AlexNet, Bag of Features (BoF), Convolutional Neural Network (CNN), indoor object recognition.
The authors are with the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Malaysia (e-mail: sriesa@gmail.com, fatinamirarikzan@gmail.com, zaidah@tmsk.uitm.edu.my, nurbaity_sabri@melaka.uitm.edu.my).
Cite: Srie Azrina Zulkeflie, Fatin Amira Fammy, Zaidah Ibrahim, and Nurbaity Sabri, "Evaluation of Basic Convolutional Neural Network, AlexNet and Bag of Features for Indoor Object Recognition," International Journal of Machine Learning and Computing vol. 9, no. 6, pp. 801-806, 2019.
Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).