Being part of a series of four talks that will be given at NTU Singapore, in this fourth talk Prof. Anderson Rocha (RECOD) explored the research field of Open-set Recognition. The talk comprises four parts, each of which lasting approximately 45 minutes, totalling three hours. Parts 1 and 2 were delivered in Day #1 (March, 6th) while the others will be delivered in Day #2 (March, 9th).
Abstract: Coinciding with the rise of large-scale statistical learning within the visual computing, forensics and security areas, there has been a dramatic improvement in methods for automated image recognition in myriad of applications ranging from, categorization, object detection, forensics, and human biometrics, among many others. Despite this progress, a tremendous gap exists between the performance of automated methods in the laboratory and the performance of those same methods in the field. A major contributing factor to this is the way in which machine learning algorithms are typically evaluated: without the expectation that a class unknown to the algorithm at training time will be experienced at test time during operational deployment.
The purpose of this talk is to introduce the audience to this difficult problem in statistical learning specifically in the context of visual computing, information forensics and security applications. Examples considering other areas will also be given for completeness. A number of different topics will be explored, including supervised machine learning, probabilistic models, kernel machines, the statistical extreme value theory, and case studies for applications related to the analysis of images.
Part 1: An introduction to the open set recognition problem
- General introduction: where do we find open set problems in visual computing, information forensics and security?
- Decision models in machine learning
- Theoretical background: the risk of the unknown
- The compact abating probability model (Scheirer et al., T-PAMI 2014)
- The Open-set Nearest Neighbors classifier (Mendes Jr. et al., Machine Learning, 2017)
Part 2: Algorithms that minimize the risk of the unknown
- Kernel Density Estimation
- 1-Class Support Vector Machines (SVMs)
- Support Vector Data Description
- 1-vs-Set Machine (Scheirer et al. T-PAMI 2013)
- PI-SVM (Jain et al. ECCV 2014)
- W-SVM (Scheirer et al. T-PAMI 2014)
- Decision Boundary Carving (Costa et al. 2014)
- Open-set Nearest Neighbors (Mendes Jr. et al. 2017)
Part 3: Case studies related to visual computing and other areas
- Image Classification/Recognition
- Visual Information Retrieval
- Detection problems (e.g., pedestrian, objects)
- Face Recognition
- Scene Analysis for Surveillance
- Source Camera Attribution
- Authorship Attribution
Part 4: Research opportunities and trends
- The open set recognition problem and new feature characterization methods (e.g., deep learning)
- Integrating open set solutions with the image characterization process directly (strongly generalizable image characterization)
- Opportunities for novelty detection and automatic addition of classes (online adaptation)
- Bringing the user into the loop (relevance feedback)
- Final considerations
If you are interested in the talk’s content, the complete set of slides is available here.
Time: 2.00pm – 3.30 pm (Seated by 1.50pm)
Venue: Demo Room, ROSE Lab, Research Techno Plaza (RTP), Level 4, Border X Block, 50 Nanyang Drive, 637553