Tag Archives: Image Aesthetics

Deep-learned Models and Photography Idea Retrieval

Intelligent Portrait Composition Assistance (IPCA)
Farshid Farhat, Mohammad Kamani, Sahil Mishra, James Wang
ACM Multimedia 2017, Mountain View, CA, USA
(Acceptance rate = 64/495 = 12.93%)

ABSTRACT: Retrieving photography ideas corresponding to a given location facilitates the usage of smart cameras, where there is a high interest among amateurs and enthusiasts to take astonishing photos at anytime and in any location. Existing research captures some aesthetic techniques such as the rule of thirds, triangle, and perspective, and retrieves useful feedbacks based on one technique. However, they are restricted to a particular technique and the retrieved results have room to improve as they can be limited to the quality of the query. There is a lack of a holistic framework to capture important aspects of a given scene and give a novice photographer informative feedback to take a better shot in his/her photography adventure. This work proposes an intelligent framework of portrait composition using our deep-learned models and image retrieval methods. A highly-rated web-crawled portrait dataset is exploited for retrieval purposes. Our framework detects and extracts ingredients of a given scene representing as a correlated hierarchical model. It then matches extracted semantics with the dataset of aesthetically composed photos to investigate a ranked list of photography ideas, and gradually optimizes the human pose and other artistic aspects of the composed scene supposed to be captured. The conducted user study demonstrates that our approach is more helpful than the other constructed feedback retrieval systems.

Please cite our paper if you are using our professional portrait dataset.

@inproceedings{Farhat:2017:IPC:3126686.3126710,
author = {Farhat, Farshid and Kamani, Mohammad Mahdi and Mishra, Sahil and Wang, James Z.},
title = {Intelligent Portrait Composition Assistance: Integrating Deep-learned Models and Photography Idea Retrieval},
booktitle = {Proceedings of the on Thematic Workshops of ACM Multimedia 2017},
series = {Thematic Workshops ’17},
year = {2017},
isbn = {978-1-4503-5416-5},
location = {Mountain View, California, USA},
pages = {17–25},
numpages = {9},
url = {http://doi.acm.org/10.1145/3126686.3126710},
doi = {10.1145/3126686.3126710},
acmid = {3126710},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {deep learning, image aesthetics, image retrieval., photographic composition, portrait photography, smart camera},
}

 

Aesthetic Rating for Photos

The developed engine gets the image and outputs a score showing the amount of the beauty inside the image! The code is also working on Android/iOS platform. The engine speed is good enough which makes it suitable for a real-time engine. The core computes aesthetic features from the image, and estimates the aesthetic score.

To be continued …

Check out > ACQUINE

The contributors: Jia Li, James Wang, Farshid Farhat.

 

Thumbnail Generation by Smart Cropping

Given any aspect ratio, oval shape, or automatically AutoThumbGen generates a thumbnail based on the input image. In fact, the most prominent part of the input image is recognized and captured by the app with a proper thumbnail size. The source code is in C. The code has been also embedded in Android via JNI and PHP by exec.

Sample photos:

 

 

The contributors: Jia Li, Farshid Farhat, James Wang.