The Online Video Contextual Advertisement User-Oriented System using Video-based Recognition

MINISTRY OF EDUCATION AND TRAINING Duy Tan University Le Nguyen Bao The Online Video Contextual Advertisement User-Oriented System using Video-based Recognition Doctor of Philosophy of Computer Science Da Nang - 2017 MINISTRY OF EDUCATION AND TRAINING Duy Tan University Le Nguyen Bao The Online Video Contextual Advertisement User-Oriented System using Video-based Recognition Major: Computer Science Code: 62.46.01.10 Doctor of Philosophy of Computer Science Scientific supervisor: Associate Professor Do Nang Toan Da Nang - 2017 Declaration of Original Work I, Le Nguyen Bao, hereby declare that the work entitled The Online Video Contextual Advertisement User-Oriented System using Video-based Recognition is my original work I have not copied from any other postgraduates’ work or from any other sources except where due references or acknowledgment is made explicitly in the text, nor has any part been written for me by another person Ph.D candidate’s signature Le Nguyen Bao i Contents Declaration of Original Work i List of Tables v List of Figures vi List of Acronyms viii Acknowledgments x Introduction 1 Background 1.1 Face detection and tracking 1.1.1 Face detection 1.1.2 Face tracking 1.2 Face recognition 1.3 Video-based face recognition 1.3.1 Haar cascade classifiers 1.3.2 Kalman filter 1.3.3 Discrete cosine transform 1.3.4 K-Means clustering 1.3.5 K-Nearest neighbors classification 1.4 Ant Colony Optimization 1.4.1 The Ant Colony Optimization Meta-heuristic 1.4.1.1 Construct Ant Solutions 1.4.1.2 Apply Local Search 1.4.1.3 Update Pheromones 1.4.2 Main ACO Algorithms 1.4.2.1 Ant System 1.4.2.2 Ant Colony System 1.4.2.3 MAX-MIN Ant System 1.4.3 Applications of Ant Colony Optimization 1.4.3.1 Applications to NP-Hard Problems 1.4.3.2 Applications to Telecommunication Networks 1.4.3.3 Applications to Industrial Problems 1.5 Summary 4 10 12 16 18 21 22 24 25 27 27 28 29 29 30 31 32 32 33 33 34 The Contextual Advertising based on Face Recognition Overview 36 ii 2.1 2.2 2.3 2.4 2.5 2.6 Online Advertisement 2.1.1 What is online advertising? 2.1.1.1 Definitions 2.1.1.2 Traditional advertising vs Online advertising 2.1.2 Advertising Metrics 2.1.3 Key Elements Contextual Advertising 2.2.1 Purchase Funnel 2.2.2 Types of Advertising 2.2.3 Payment Models 2.2.4 Research Challenges and Opportunities Display Advertising 2.3.1 Research Challenges and Opportunities in Display Advertising 2.3.2 New Trends and Issues Elevator Advertising 2.4.1 Creative Elevator Advertisements 2.4.2 Elevator Screen Related Works Summary Some Techniques Improve the Efficiency of Face 3.1 Video-based face recognition used FS problem 3.1.1 Video-based face recognition problem 3.1.2 Feature selection problem 3.2 Our framework of face recognition 3.2.1 Pseudo Zernike Moment Invariant 3.2.2 Discrete Wavelet Transform 3.2.3 k-Nearest Neighbor Classifier 3.3 MMAS proposed for feature selection problem 3.3.1 Construct Ant solutions 3.3.2 Update pheromones 3.3.3 Our algorithm implementation 3.4 Experiment and Results 3.4.1 Experiment implementation 3.4.1.1 ORL database 3.4.1.2 AR database 3.4.1.3 FERET database 3.4.1.4 GEORGIA TECH database 3.4.1.5 LFW database 3.4.2 Case study 3.4.3 Case study 3.4.4 Case study 3.4.5 Case study 3.5 Summary iii Recognition 37 37 38 40 42 42 43 44 45 46 48 51 52 54 55 55 56 59 62 63 63 63 64 65 66 68 69 71 71 71 74 75 75 75 76 77 78 79 80 82 82 85 86 The Online Video Contextual Advertisement User-Oriented System using Video-based Recognition Elevator 87 4.1 Framework for online video contextual advertisement user-oriented in elevator system 87 4.1.1 Identifying and classifying objects based on images captured from the Camera 88 4.1.2 Accessing video database under classified objects 90 4.1.3 Transferring video content 91 4.2 Real Time Multimedia Protocol 95 4.2.1 Streaming 96 4.2.1.1 Traditional Streaming 97 4.2.1.2 Progressive Download 97 4.2.1.3 Adaptive streaming 97 4.2.2 Real-Time Networked Multimedia 98 4.2.3 Real-time Streaming Media Protocols 99 4.2.3.1 Basics of streaming protocols 99 4.2.3.2 Datagram Protocol 101 4.2.3.3 Multicast IP protocol 102 4.2.3.4 Real-Time Streaming Protocol 103 4.2.3.5 SMIL Protocol 106 4.3 Experiment and results 107 4.3.1 Experiment implementation 107 4.3.1.1 Honda/UCSD database 107 4.3.1.2 MoBo Dataset 107 4.3.2 Case study 108 4.3.3 Case study 109 4.4 Summary 111 Discussion and Conclusions 112 List of Publications 115 Reference 116 iv List of Tables 1.1 1.2 1.3 1.4 1.5 1.6 1.7 Features face detection approaches The challenges of boosting learning for face detection The learning schemes for face detection Face tracking approaches Video based face recognition approaches A non-exhaustive list of successful ACO algorithms List of applications of ACO algorithms grouped by problem type 2.1 2.2 2.3 Some differences between traditional and online advertising 40 Summary of approaches and methods for face recognition problem 60 Summary of approaches and methods in feature selection problem 61 3.1 3.2 3.3 3.4 3.5 3.6 The ORL face database properties The AR face database properties The FERET face database properties The GEORGIA TECH face database properties The LFW face database properties The comparison performance of meta-heuristic algorithms with PZMI feature subsets in Case study 3.7 Evaluation recognition rate (%) of meta-heuristic algorithms with PZMI feature subset sizes in Case study 3.8 The comparison performance of algorithms with 1D-DWT feature subsets in Case study 3.9 Evaluation recognition rate (%) of each algorithm with 1D-DWT feature subset sizes in Case study 3.10 Comparison of recognition accuracy between MMAS-PZMI and MMASDWT algorithm with difference face data sets v 11 25 35 76 77 78 79 80 81 81 84 84 85 List of Figures 1.1 1.2 1.3 1.4 Face recognition processing flow Face recognition process Process of face recognition in video Examples of Haar-like features Their values represent the intensity differences between the black and the white areas 1.5 Integral image (a) The integral at (x , y) is the sum of all pixels up to (x , y)in the original image (b) The area of rectangle D results to ii (A+B +C +D)−ii (A+C )−ii (A+B )+ii (A) Each ii () can be determined with a single array 1.6 Structure of the classifier cascade ”Yes” and ”no” denote if the subwindow successfully passed the previous stage 1.7 Two out of four features evaluated in the first stage of the face detection cascade used in this work Both features embody the observation that the forehead and cheeks of a person are usually brighter than the eye region because the eyes are located further aback 1.8 Overview of the Kalman filter as a predictor-corrector algorithm 1.9 Cosine basis functions of the discrete cosine transform for input size 8×8 The frequency of the basis functions increases from top left (0, 0) to bottom right (8, 8) 1.10 Discrete cosine transform of an 88 pixels image patch The coefficients represent the basis functions depicted in Figure 1.9 1.11 Average energy of all 64 blocks of the image in Figure 1.10(a) The DC coefficient has been removed to allow meaningful scaling 1.12 The Discrete cosine transform coefficients are serialized according to a zig-zag pattern 8 11 12 13 14 15 16 19 20 20 21 2.1 2.2 2.3 2.4 2.5 Creative Elevator Advertisements Elevator Screen Elevator Advertisements Screen The integrated LCD screen play video system Application of the integrated LCD screen play video system 57 58 58 58 59 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 Framework of face recognition used MMAS algorithm Digraph G = (E , V ) for feature set F Sample images of the ORL face database Sample images of the AR face database Sample images of the FERET face database Sample images of the GEORGIA TECH face database Sample images of the LFW face database Assessment of the recognition rate of PZMI features Assessment of the recognition rate of 1D-DWT features Evaluation performance of proposed algorithms 65 71 75 76 78 79 80 82 83 85 vi 3.11 Assessment of VbFR approaches execution on Honda/UCSD and CMUMoBo datasets 86 4.1 4.2 4.3 4.4 4.5 4.6 4.7 The online video contextual advertisement system proposed Object recognition model from the camera Video database access model based metadata structure Transferring video content protocols Sample images of the MoBo face database Some face images extracted from the Honda/UCSD Video Database Comparision the recognition rate (%) approaches on Honda/UCSD and CMU MoBo datasets 4.8 The proposed system automatically detects object and stores the object in the database 4.9 The system play default video player when not detected object 4.10 Auto detect, identifier object and choose play the most suitable video 4.11 Detecting multiple object identifiers concurrent vii 88 89 91 93 107 108 108 109 109 110 110 List of Acronyms Acronym AS ACO ACS CPC CPM CPA DWT DCC FS ICA KNN KPA HTML HDS LDA LLP LLE LNMF LNDM MDS MFA MMAS NNC NDMP P2P PCA PZMI QoS SVM SMIL RTSP RTMP RTP RTCP What (it) Stands For Ant System Ant Colony Optimization Ant Colony System Cost Per Click Cost per Mille Cost Per Action Discrete Wavelet Transform Discriminative Canonical Correlations Feature Selection Independent Component K-nearest neighbors Kernel Principal Angles Hypertext Markup Language HTTP Dynamic Streaming Linear Discriminant Locality Preserving Projections Locally Linear Embedding Local Non-negative Matrix Factorization Learning Neighborhood Discriminative Manifolds Multidimensional Scaling Marginal Fisher Analysis MAX-MIN Ant System Nearest Neighbor Classifier Neighborhood Discriminative Manifold Projection Peer-to-peer Principal component analysis Pseudo Zernike Moment Invariant Quality of Service Support vector machine Synchronized Multimedia Integration Language Real-Time Streaming Protocol Real Time Messaging Protocol Real-time Transport Protocol Real-time Transport Control Protocol viii 4.3.2 Case study In the first experiment, we used two standard test datasets include Honda/UCSD and CMU MoBo Figure.4.6 shows sample extracted face images from the Honda/UCSD Video Database The faces pattern is used for training and identification of age Figure 4.6: Some face images extracted from the Honda/UCSD Video Database Figure.4.7 shows the recognition rate (%) experimental results of some typical methods of video-based face recognition on the Honda/UCSD and CMU MoBo databases Figure 4.7: Comparision the recognition rate (%) approaches on Honda/UCSD and CMU MoBo datasets 108 4.3.3 Case study The second experiment, to verify the proposed model, we have installed trial identification system object from the camera and coaching based on neural network to improve the accuracy of the ability to detect objects Figure 4.8: The proposed system automatically detects object and stores the object in the database In case, the system could not detect any objects from camer, the system will automatically plays default video as Figure.4.9 Figure 4.9: The system play default video player when not detected object 109 When the object is detected, the system will automatically identify objects and provide the most relevant video as Figure.4.10 Figure 4.10: Auto detect, identifier object and choose play the most suitable video When the multiple objects are detected, the system will automatically evaluate the object to select the most appropriate video for the object identifier Figure 4.11: Detecting multiple object identifiers concurrent 110 4.4 Summary In this chapter, we have presented proposal of the model of advertising system automatically customized according to customer objects in elevator From the actual images received from the camera, the system will analyze the objects based on the given characteristic set to determine the appropriate class of customer Based on customer class is defined, the system will access to database of multimedia advertising and automatic selection, delivery of appropriate contents For each stage, we have focused on analysis and evaluation of techniques used to enhance the viability and effectiveness of the system The experimental results shows that our approach can be detect exactly faces from standard video database Out system can accurately identify the object and choose suitable video directly from the camera in realtime In the future, we will develop and conduct effective analysis and assessment of real systems with many different techniques in each phase The study will look forward to building a complete system supporting accurate analysis of objects and transmission speed of video over the network quickly The results of this studies have been published in [1*] and [2*] (See in list of publications) 111 Discussion and Conclusions In this chapter, we present a final discussion of the dissertation, including directions for future research and a review of our primary contributions and conclusions Discussion 1) Proposed the model of advertising system automatically customized according to customer objects From the actual images received from the camera, the system will analyze the objects based on the given characteristic set to determine the appropriate class of customer Based on customer class is defined, the system will access to database of multimedia advertising and automatic selection, delivery of appropriate contents For each stage, we have focused on analysis and evaluation of techniques used to enhance the viability and effectiveness of the system The experimental results shows that our approach can be detect exactly faces from standard video database Out system can accurately identify the object and choose suitable video directly from the camera in realtime In the future, we will develop and conduct effective analysis and assessment of real systems with many different techniques in each phase The study will look forward to building a complete system supporting accurate analysis of objects and transmission speed of video over the network quickly 2) Proposed MMAS-PZMI and MMAS-DWT algorithms solve the FS problem used PZMI and DWT feature for VbFS The features set are represented by digraph G(E , V ) Each node used to show the features, and the ability to choose a combination of features is presented the edges connecting between two adjacent nodes The heuristic information extracted from the selected feature vector as ant’s pheromone The feature subset optimal is selected by the shortest length features and best presentation of classifier The experiments were analyzed on face recognition show that our algorithm can be easily applied without the priori information of features The performance evaluated of our algorithm is better than previous approaches for FS The 112 best subset used to classify the face recognition The experiments were analyzed on FS shows that our algorithm can be easily applied without the priori information of features The performance evaluated shows that our algorithm proposed better than previous approaches for Video-based recognition based on FS problem Future Works The issues mentioned in the dissertation covered quite a lot of content, content for each respective program presented may find problems that can used to recommend content to guide research save for the next project It shows the openness of the research problems that students mentioned in the thesis Some open direction of the dissertation can be further studies: 1) Mobile advertising (advertising on cell-phones, be it SMS-based, applicationbased or browserbased) is one of the fastest growing segments in digital advertising and comes with its own challenges of performance and relevance measurements (e.g., clicks are uncommon in mobile 2) Social advertising leverages historically offline dynamics such as peer-pressure, recommendations, and other forms of social influence to target ads based an individuals social network or affinity network 113 List of publications 1* Le Nguyen Bao, Le Van Chung, Do Nang Toan (2016) A Proposed Framework for the Online Video Contextual Advertisement User-Oriented System using Video-based Face Recognition, International Journal of Applied Engineering Research, ISSN 0973-4562 Vol.11(15), pp.8609-8617 (Scopus) 2* Le Nguyen Bao, Dac-Nhuong Le, Le Van Chung, Gia Nhu Nguyen (2016) Performance Evaluation of Video-Based Face Recognition Approaches for Online Video Contextual Advertisement User-Oriented System Information Systems Design and Intelligent Applications, Vol.435, 287-295 Springer DOI:10.1007/978-81-322-2757-1 29 (ISI Proceeding/Scopus) 3* Le Nguyen Bao, Dac-Nhuong Le, Gia Nhu Nguyen, and Do Nang Toan (2016), Optimizing Selection of PZMI Features based on MMAS Algorithm for Face Recognition of the Online Video Contextual Advertisement UserOriented System, International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making (IUKM 2016) Lecture Notes in Artificial Intelligence, Springer, Vol.9978, pp.318-330 ISBN 978-3-31949045-8 DOI: 10.1007/978-3-319-49046-5 (ISI Proceeding/Scopus) 4* Le Nguyen Bao, Dac-Nhuong Le, Gia Nhu Nguyen, and Nilanjan Dey (2017), MMAS Algorithm for Features Selection using 1D-DWT for Videobased Face Recognition in the Online Video Contextual Advertisement UserOriented System, Special Issue: Content Protection and Management in E-commerce: Approaches to Multimedia Security, Journal of Global Information Management (JGIM), ISSN: 1062-7375, Vol.25(4), pp.103-124 DOI: 10.4018/JGIM.2017100107 (SSCI IF 0.517) 5* Le Nguyen Bao, Dac-Nhuong Le, Gia Nhu Nguyen, Vikrant Bhateja, Suresh Chandra Satapathy (2017), Optimizing Feature Selection in Videobased Recognition using Max-Min Ant System for the Online Video Contextual Advertisement User-Oriented System, Journal of Computational Science, Vol.21, pp.361-370, Elsevier DOI: 10.1016/j.jocs.2016.10.016 ISSN: 1877-7503 (SCIE IF 1.748) 114 6* Vikrant Bhateja., Moin, A., Srivastava, A., Le Nguyen Bao, Lay-Ekuakille, A., and Dac-Nhuong Le (2016) Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimers disease Review of Scientific Instruments, 87(7), 074303 DOI: 10.1063/1.4959559 (SCIE IF 1.515) 7* Vikrant Bhateja, Abhishek Tripathi, Aditi Sharma, Le Nguyen Bao, Suresh Chandra Satapathy, Dac-Nhuong Le, and Nguyen Gia Nhu (2016), Ant Colony Optimization based Anisotropic Diffusion Approach for Despeckling of SAR Images, International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making (IUKM 2016) Lecture Notes in Artificial Intelligence, Springer, Vol.9978, pp.386-393 ISBN 978-3-319-49045-8 DOI: 10.1007/978-3-319-49046-5 (ISI Proceeding/Scopus) 115 Bibliography [1] Andrea F Abate, Michele Nappi, Daniel Riccio, and Gabriele Sabatino 2d and 3d face recognition: A survey Pattern Recognition Letters, 28(14):1885– 1906, 2007 [2] Salem Alelyani, Jiliang Tang, and Huan Liu Feature selection for clustering: A review Data Clustering: Algorithms and Applications, 29, 2013 [3] Hussein Almuallim and Thomas G Dietterich Learning with 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machine learning models to deliver maximum efficiency to the system The structure of the thesis is organized into four chapters: • In the first chapter, the thesis focuses... 76 77 78 79 80 82 82 85 86 The Online Video Contextual Advertisement User-Oriented System using Video- based Recognition Elevator 87 4.1 Framework for online video contextual advertisement user-oriented