Open Set Recognition Problem Research Articles

Deep Open Set Recognition Using Dynamic Intra-class Splitting

This paper provides a generic deep learning method to solve open set recognition problems. In open set recognition, only samples of a limited number of known classes are given for training. During inference, an open set recognizer must not only correctly classify samples from known classes, but also reject samples from unknown classes. Due to these specific requirements, conventional deep learning models that assume a closed set environment cannot be used. Therefore, special open set approaches were taken, including variants of support vector machines and generation-based state-of-the-art methods which model unknown classes by generated samples. In contrast, our proposed method models unknown classes by atypical subsets of training samples. The subsets are obtained through intra-class splitting (ICS). Based on a recently proposed two-stage algorithm using ICS, we propose a one-stage method based on alternating between ICS and the training of a deep neural network. Finally, several experiments were conducted to compare our proposed method with conventional and other state-of-the-art methods. The proposed method based on dynamic ICS showed a comparable or better performance than all considered existing methods regarding balanced accuracy.

Open Set Recognition of Timber Species Using Deep Learning for Embedded Systems

Reliable and rapid identification of timber species is a very relevant issue for many countries in South America and especially for Peru, which is the second country with the largest extent of tropical forest, and that is because this issue is a necessity in order to develop an effective management of the forest resources, such as inspection and control of the timber commerce. Since current methods of identification are based on a closed set recognition approach, they are not reliable enough to be used in a practical application because scenarios of identification of timber species are by nature an open set recognition problem. For that reason, in this work we propose a convolutional neural network that has two main characteristics, being able to run in a real-time embedded system and being able to handle the open set recognition problem, that is, this model can discriminate between known and unknown species. In order to evaluate it, tests are performed in two timber species datasets and some experiments are developed in the embedded system Raspberry Pi3B+ to measure energy consumption. The results present high metrics, which means that it manages to discriminate the unknown species with accuracy and F1 score above 91% for two sets of images used. In addition to this, our proposed model obtain lower maximum power value (10-12%) and computational resource usage (5-13%) than a classical convolutional model and MobileNetsV2 measured on the Raspberry Pi3B+.

Rethinking compact abating probability modeling for open set recognition problem in Cyber-physical systems

Abstract Machine learning (ML) components, especially convolutional neural network (CNN) based components, are becoming increasingly vital in cyber-physical systems (CPS), existing great influence on the dependability of CPS. Different to the popular ML model with close set hypothesis, which limits the classes of testing samples to seen classes in training samples, ML in CPS should face the problem of real-world recognition. Open set recognition (OSR) is a more suitable model for improving the dependability of ML components, which allows the presence of unknown classes at testing time. However, the ability to reject those unknown classes is concurrently required in the model. In this paper, we find the defect in compact abating probability application to most CNN-based OSR model. In order to mitigate the defect, two different methods, OpenSoftMax and OPEB are respectively proposed. Finally, extensive experiments on caltech256 data set have been conducted to verify the effectiveness and efficiency of our proposals. Compared to the well-known CNN-based OSR method OpenMax, our first method OpenSoftMax achieves 15.55% average accuracy performance gain and 28.35% F1-score performance gain, while our second method OPEB achieves 13.17% average accuracy performance gain and 25.70% F1-score performance gain.

Data-Fusion Techniques for Open-Set Recognition Problems

Most pattern classification techniques are focused on solving closed-set problems in which a classifier is trained with samples of all classes that may appear during the testing phase. In many situations, however, samples of unknown classes, i.e., whose classes did not have any example during the training stage, need to be properly handled during testing. This specific setup is referred to in the literature as open-set recognition. Open-set problems are harder as they might be ill-sampled, not sampled at all, or even undefined. Differently from existing literature, here we aim at solving open-set recognition problems combining different classifiers and features while, at the same time, taking care of unknown classes. Researchers have greatly benefited from combining different methods in order to achieve more robust and reliable classifiers in daring recognition conditions, but those solutions have often focused on closed-set setups. In this paper, we propose the integration of a newly designed open-set graph-based optimum-path forest (OSOPF) classifier with genetic programming (GP) and majority voting fusion techniques. While OSOPF takes care of learning decision boundaries more resilient to unknown classes and outliers, GP combines different problem features to discover appropriate similarity functions and allows a more robust classification through early fusion. Finally, the majority-voting approach combines different classification evidence from different classifier outcomes and features through late-fusion techniques. Performed experiments show the proposed data-fusion approaches yield effective results for open-set recognition problems, significantly outperforming existing counterparts in the literature and paving the way for investigations in this field.

Sparse Representation-Based Open Set Recognition.

We propose a generalized Sparse Representation-based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms.

Open Set Fingerprint Spoof Detection Across Novel Fabrication Materials

A fingerprint spoof detector is a pattern classifier that is used to distinguish a live finger from a fake (spoof) one in the context of an automated fingerprint recognition system. Most spoof detectors are learning-based and rely on a set of training images. Consequently, the performance of any such spoof detector significantly degrades when encountering spoofs fabricated using novel materials not found in the training set. In real-world applications, the problem of fingerprint spoof detection must be treated as an open set recognition problem where incomplete knowledge of the fabrication materials used to generate spoofs is present at training time, and novel materials may be encountered during system deployment. To mitigate the security risk posed by novel spoofs, this paper introduces: 1) the use of the Weibull-calibrated SVM (W-SVM), which is relatively robust for open set recognition, as a novel-material detector and a spoof detector and 2) a scheme for the automatic adaptation of the W-SVM-based spoof detector to new spoof materials that leverages interoperability across classifiers. Experiments conducted on new partitions of the LivDet 2011 database designed for open set evaluation suggest: 1) a 97% increase in the error rate of the existing spoof detectors when tested using new spoof materials and 2) up to 44% improvement in spoof detection performance across spoof materials when the proposed adaptive approach is used.

Open set source camera attribution and device linking

Camera attribution approaches in digital image forensics have most often been evaluated in a closed set context, whereby all devices are known during training and testing time. However, in a real investigation, we must assume that innocuous images from unknown devices will be recovered, which we would like to remove from the pool of evidence. In pattern recognition, this corresponds to what is known as the open set recognition problem. This article introduces new algorithms for open set modes of image source attribution (identifying whether or not an image was captured by a specific digital camera) and device linking (identifying whether or not a pair of images was acquired from the same digital camera without the need for physical access to the device). Both algorithms rely on a new multi-region feature generation strategy, which serves as a projection space for the class of interest and emphasizes its properties, and on decision boundary carving, a novel method that models the decision space of a trained SVM classifier by taking advantage of a few known cameras to adjust the decision boundaries to decrease false matches from unknown classes. Experiments including thousands of unconstrained images collected from the web show a significant advantage for our approaches over the most competitive prior work.

Toward Open Set Recognition

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 paper 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 toward 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.