MMAS Algorithm for Features Selection Using 1D-DWT for Video-Based Face Recognition in the Online Video Contextual Advertisement User-Oriented System

MMAS Algorithm for Features Selection Using 1D-DWT for Video-Based Face Recognition in the Online Video Contextual Advertisement User-Oriented System

Le Nguyen Bao (Duy Tan University, Danang, Vietnam), Dac-Nhuong Le (Duy Tan University, Danang, Vietnam & Haiphong University, Haiphong, Vietnam), Gia Nhu Nguyen (Duy Tan University, Vietnam), Le Van Chung (Duy Tan University, Danang, Vietnam) and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India)
Copyright: © 2017 |Pages: 22
DOI: 10.4018/JGIM.2017100107
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Face recognition is an importance step which can affect the performance of the system. In this paper, the authors propose a novel Max-Min Ant System algorithm to optimal feature selection based on Discrete Wavelet Transform feature for Video-based face recognition. The length of the culled feature vector is adopted as heuristic information for ant's pheromone in their algorithm. They selected the optimal feature subset in terms of shortest feature length and the best performance of classifier used k-nearest neighbor classifier. The experiments were analyzed on face recognition show that the authors' algorithm can be easily implemented and without any priori information of features. The evaluated performance of their algorithm is better than previous approaches for feature selection.
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1. Introduction

Today, advertising has been a convergence and new development step when moving to multimedia communicating model with the high interoperability via the Internet. This is also a challenge for the dynamic new forms of advertising, in contrast with the unilateral form of direct advertising as people has become passive with the advertising systems. The advertising customization, with the ability to update in real time to change advertising text according to the full context of a search or the website someone is seeing, like: Google AdWords, Google AdSense, etc. (Hua, 2010; Hassan, & Privitera, 2016). This is contextual display advertising or targeting advertising technology and is very effective. With the common forms of advertising like logos, banners, pop-ups, etc., from time to time; users accessing the site can see the ads. However, the drawback of this method is the depending on irregular traffic, difficulty to control, low-orientation, advertising contents must try harder to hit the target objects because the ad is only located on a fixed website and also appears in the irrelevant articles, columns. The next technology is displaying contextual advertisement content on the website or other media such as mobile phone to approach user, based on the context of the article, geographic location, and time of user accessing to the ad and accessing habits of potential customers. Contextual advertising is a form of internet marketing that is directed towards specific websites based off the content of the ad, the website and the user. These advertisements are chosen by a variety of automated systems in order to target the user with content that pertains to them (Ghosh, Mahdian, McAfee et al., 2015; Bagherjeiran, Hatch, Ratnaparkhi et al., 2015).

As online advertising has exploded in the past decade, it has often been contrasted with traditional media such as television, print, and radio. The online video contextual advertisement user-oriented system which we proposed to make the content of ads relevant and truly useful for customers in each specific context through object analysis identifier acquired from the camera. The system is a combination of image processing and identification with multimedia communications and networking streaming architecture used Meta-data structure to store video data plus machine learning models to deliver maximum efficiency to the system. Our system represented in Figure 1 (Le, Van Chung, & Nguyen, 2016).

Figure 1.

Online Video Contextual Advertisement User-Oriented System


The system consists of three major phases as follows:

  • Phase 1: Identifying and classifying objects based on images captured from the Camera.

  • Phase 2: Accessing video database under classified objects.

  • Phase 3: Transferring video content.

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