Smart Image Scoring

Image Aesthetics Scoring Engine

The developed engine gets the image and outputs a score showing the amount of the beauty inside the image! The code is working on Android/iOS platform,

The engine speed is about 100-200 ms which makes it suitable for a real-time engine. The core exploits GPU to compute aesthetic features from the image, and estimate the aesthetic score.

Android, Computer Vision, Demo, iOS, Research, Aesthetic Feature Extraction, Aesthetics Scoring, Android, GPU, Image Aesthetics, iOS, Renderscript, Scoring Engine.

Maryam Mirzakhani, the only women to win Fields Medal in Math, professor at Stanford

Maryam Mirzakhani was first women to win Math Fields Medal. She would be in our mind forever.

RIP #MaryamMirzakhani

in Farshid Farhat 's Twitter

Transferring Deep-learned Models

Integrating Deep-learned Models and Photography Idea Retrieval

Intelligent Portrait Composition Assistance (IPCA) – Integrating Deep-learned Models and Photography Idea Retrieval, Farshid Farhat, Mohammad Kamani, Sahil Mishra, James Wang, ACM Multimedia 2017, Mountain View, CA, USA.

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 perspectiveness, 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.

Full Text @ intelligent-portrait-composition-assistance-deep-learning-image-retrieval

Dataset

Code

Art, Computer Vision, Conference Paper, Deep Learning, Image Processing, Machine Learning, Pattern Recognition, Photography, Research, Thesis and tagged ACM Multimedia, ACMM, Aesthetics, AI, Art Theory, Artificial Intelligence, CBIR, Computer Vision, Content-based Image Retrieval, DEEP LEARNING, Deep-Learned Model Transfer, Human Pose, Human Pose Estimation, Image Aesthetics, Image Processing, Image Retrieval, Intelligent Portrait Composition Assistance, IPCA, Machine Learning, Object Detection, Pattern Recognition, Penn State Deep Learning, Photography, Photography Idea, Portrait, Portrait Composition, Portrait Dataset, Portrait Photography, Pose Estimation, Pose Recommendation, Scene Parsing