Object-Level Level Video Advertising: An Optimization Framework
Abstract: In this paper, we present new models and algorithms for object-level object video advertising. A framework that aims to embed content content-relevant relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize mize 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 ext extract ract 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 n novel ovel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for object object-level video advertising.