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Discriminative Image Representation based on Multi-Cues for Computational Advertising

Volume 14, Number 7, July 2018, pp. 1411-1420
DOI: 10.23940/ijpe.18.07.p4.14111420

Zhize Wua, Shouhong Wanb, and Ming Tana

aDepartment of Computer Science and Technology, Hefei University, Hefei, 230601, China
bSchool of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China

(Submitted on April 13, 2018; Revised on May 25, 2018; Accepted on June 16, 2018)

Abstract:

Image representation is a key step in image advertising recommendations. Traditional image representation methods, based on the local description, generate a histogram of visual words to represent images. However, it is very difficult to establish a discriminative and descriptive codebook with the local description only. Therefore, we propose a novel image representation method by integrating visual saliency, color feature and local description. Moreover, the proposed multi-cues image representation has been applied to a new image advertising scenario, i.e., delivering image advertisements in a list of images, such as the results of an image search. To evaluate our proposal, we have crawled a dataset, named Pop2016, which consists of image lists and advertising images with 31 pop labels. The performance of the advertising recommendations is measured in terms of the precision@n and the mean average precision. Experimental results show that the proposed algorithm outperforms several traditional methods.

 

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