Multimedia Tools and Applications

, Volume 77, Issue 17, pp 22173–22184 | Cite as

Adaptive recommendation for photo pose via deep learning

  • Tong Hao
  • Qian Wang
  • Dan Wu
  • Jin-Sheng Sun


With the development of image acquisition devices and the popularity of smart phones, more and more people would like to upload their photos to diverse social networks. It is hard to guarantee the quality and artistry of these photos because of not everyone is a professional photographer. In order to handle this problem and further help each common user to improve the beauty of photos, we propose an intelligent photo pose recommendation method to recommended professional photo pose according to everyone’s posture in viewfinder. Firstly, the CNN model (VGG-16) is utilized to extract the global features for each photo. Secondly, the salient region detection method is leveraged to extract the regions of interest in each photo. To represent the edge distribution in the local regions, we extract the histogram of oriented gradients. Finally, we propose an effective feature fusion method based on CCA to generate the global visual features for each photo. We implement the Euclidean distance to handle the similarity measure between uploaded photos and the professional photos. The most similar professional photo will be utilized to guide user photo composition. In order to evaluate the performance of the proposed method, we collected a set of professional photos form some professional photography websites. The comparison experiments and user study demonstrate the superiority of the proposed approach.


Photo pose recommendation Deep learning Feature fusion 



This work was funded by National High-Tech Research and Development Program of China (863 programs, 2012AA10A401), Grants of the Major State Basic Research Development Program of China (973 programs, 2012CB114405), National Natural Science Foundation of China (31770904,21106095), National Key Technology R & D Program (2011BAD13B07, 2011BAD13B04), Tianjin Applied Basic and Advanced Technology Research Program (15JCYBJC30700), Project of introducing one thousand high level talents in three years(5KQM110003), Tianjin Normal University Academic Innovation Promotion Program for Young Teachers (52XC1403) and Tianjin Innovative Talent Training Program (ZX110170).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Tianjin Key Laboratory of Animal and Plant Resistance/College of Life ScienceTianjin Normal UniversityTianjinChina
  2. 2.Tianjin Aquatic Animal Infectious Disease Control and Prevention CenterTianjinChina

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