Journal ArticleDOI
Object-Level Video Advertising: An Optimization Framework
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TLDR
New models and algorithms for object-level video advertising that aims to embed content-relevant ads within a video stream is investigated and a heuristic algorithm is developed to solve the proposed optimization problem.Abstract:
In this paper, we present new models and algorithms for object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize intrusiveness to viewers when ads are inserted in a video. For human clothing advertising, we design a deep convolutional neural network using face features to recognize human genders in a video stream. Human parts alignment is then implemented to extract human part features that are used for clothing retrieval. Second, we develop a heuristic algorithm to solve the proposed optimization problem. For comparison, we also employ the genetic algorithm to find solutions approaching the global optimum. Our novel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for object-level video advertising.read more
Citations
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Journal ArticleDOI
A transfer convolutional neural network for fault diagnosis based on ResNet-50
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Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm
TL;DR: The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts, especially for real-world engineering problems, and is more competitive than other reported methods in terms of both convergence rate and computational efforts.
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Artificial intelligence in marketing: Systematic review and future research direction
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Structural damage detection using finite element model updating with evolutionary algorithms: a survey
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Journal ArticleDOI
Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM
Tanveer Hussain,Khan Muhammad,Amin Ullah,Zehong Cao,Sung Wook Baik,Victor Hugo C. de Albuquerque +5 more
TL;DR: This article achieves MVS by integrating deep neural network based soft computing techniques in a two-tier framework that extracts deep features from each frame of a sequence in the lookup table and passes them to deep bidirectional long short-term memory (DB-LSTM) to acquire probabilities of informativeness and generates a summary.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Dropout: a simple way to prevent neural networks from overfitting
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Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
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Journal ArticleDOI
Object Detection with Discriminatively Trained Part-Based Models
TL;DR: An object detection system based on mixtures of multiscale deformable part models that is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges is described.
Journal ArticleDOI
Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
TL;DR: A simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent.