Viability of Viola-Jones method for the problem of image classification

In this paper we study combination of Viola-Jones classifier with deep convolutional neural network as an approach to the problem of object detection and classification. It is well known that Viola-Jones detectors are fast and accurate in detection of vast variety of different objects. On the other hand, methods based on neural network usage demonstrate high accuracy in the problems of image classification. The main goal of this paper is to study viability of Viola-Jones classifier in problem of image classification. The first part of both algorithms is the same: we will use Viola-Jones classifier to find object bounding rectangle in the image. The second part of the algorithms is different: we will compare usage of Viola-Jones classifier with convolutional neural network-based classifier. We will provide speed and accuracy comparison between these two algorithms.

[1]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[2]  Gang Hua,et al.  A convolutional neural network cascade for face detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Dmitry P. Nikolaev,et al.  Viola-Jones based hybrid framework for real-time object detection in multispectral images , 2015, International Conference on Machine Vision.

[4]  Dmitry P. Nikolaev,et al.  Fast integer approximations in convolutional neural networks using layer-by-layer training , 2017, International Conference on Machine Vision.

[5]  Elena Limonova,et al.  Computational optimization of convolutional neural networks using separated filters architecture , 2020, ArXiv.

[6]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[9]  Vladimir V. Arlazarov,et al.  Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection , 2018, International Conference on Machine Vision.

[10]  Junwei Han,et al.  A Survey on Object Detection in Optical Remote Sensing Images , 2016, ArXiv.

[11]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Kensuke Yokoi,et al.  APAC: Augmented PAttern Classification with Neural Networks , 2015, ArXiv.