Towards Open Set Recognition
Walter J. Scheirer, Anderson Rocha, Archana Sapkota, and Terrance E. Boult
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vol. 36, No. 7, July 2013.
Abstract. To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of “closed set” recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is “open set” recognition, where incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. This work explores the nature of open set recognition, and formalizes its deﬁnition as a constrained minimization problem.
The open set recognition problem is not well addressed by existing algorithms because it requires strong generalization. As a step towards a solution, we introduce a novel “1-vs-Set Machine,” which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. This methodology applies to several different applications in computer vision where open set recognition is a challenging problem, including object recognition and face veriﬁcation. We consider both in this work, with large scale cross-dataset experiments performed over the Caltech 256 and ImageNet sets, as well as face matching experiments performed over the Labeled Faces in the Wild set. The experiments highlight the effectiveness of machines adapted for open set evaluation compared to existing 1-class and binary SVMs for the same tasks.
Index Terms. Open Set Recognition, 1-vs-Set Machine, Machine Learning, Object Recognition, Face Verification, Support Vector Machines.
Clone the source from github : libSVM-onevset
UPDATE: to support our CVPR2015 paper on “Toward Open World”, we wanted to compare with 1-vs-set, but the libSVM-onevset version was too slow..(weeks) and so we developed a multi-class extension to liblinear. Now Clone the ne source at https://github.com/Vastlab/liblinear.git
Here are some 1-vs-Set-Machine explanatory animations.
Data Set Features
Data set features we have used in the paper to validate the proposed 1-vs-Set-Machine
- HOG Features, Open Universe of 88 Classes
- HOG Features, Open Universe of 212 Classes
- LBP-like Features, Open Universe of 88 Classes
- LBP-like Features, Open Universe of 212 Classes
- LBP-like Features, Face Verification