Face recognition has wide ranging applications. In this project we focus on images taken by regular users with the constraint that training examples could be quite limited in number.
– Get familiar with the state-of-the-art. Two references to start with are:
(a) Regularized Robust Coding for Face Recognition
(b) Fast L1 Minimimzation Algorithms for Robust Face Recognition
– Implement, test, and compare one state-of-the-art technique
– Working implementation, preferably in C/C++
– Knowledge of image processing, computer vision algorithms
– Knowledge of programming in Matlab, C/C++
Type of work:
65% development and testing
Radhakrishna Achanta (firstname.lastname@example.org)
Harvesting the discriminative patches of the keywords
Synopsis: With the help of image search engines like Google and Bing, one can easily download hundreds of images for a keyword. However, why are these images correspond to the keyword? What makes a keyword different from other keywords in images? In this project we aim at finding the discriminative patches that defines the keyword in images. The student are required to explore the visual features of the image patches and using machine learning techniques to find the discriminative patches. So by the end of the project, the students should understand “what makes paris look like paris”!
In this project you will:
- Build up a dataset of images for several keywords
- Extract and explore the visual features of the patches
- Use machine learning techniques to find the discriminative patches for each keyword
 Doersch, C., Singh, S., Gupta, A., Sivic, J., & Efros, A. (2012). What makes Paris look like Paris?. ACM Transactions on Graphics, 31(4).
 Singh, Saurabh, Abhinav Gupta, and Alexei A. Efros. “Unsupervised discovery of mid-level discriminative patches.” Computer Vision–ECCV 2012. Springer Berlin Heidelberg, 2012. 73-86.
 Doersch, C., Gupta, A., & Efros, A. A. (2013). Mid-level visual element discovery as discriminative mode seeking. In Advances in Neural Information Processing Systems (pp. 494-502).
 Sun, J., & Ponce, J. (2013, December). Learning discriminative part detectors for image classification and cosegmentation. In Computer Vision (ICCV), 2013 IEEE International Conference on (pp. 3400-3407). IEEE.
 Lindner, A., Bonnier, N., & Süsstrunk, S. (2012, January). What is the color of chocolate?–Extracting color values of semantic expressions. In Conference on Colour in Graphics, Imaging, and Vision.
Deliverables: In the end of the semester, the student should provide a written report on the work done as well as the program that could find the discriminative patches of the images.
Prerequisites: Knowledge of image processing/computer vision and machine learning, programming with Matlab or C/C++ using OpenCV.
Type of work: 50% research and 50% implementation.
Level: MS, semester project.
Supervisor: Bin Jin (email@example.com)