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Local Novelty Detection in Multi-class Recognition Problems

Paul Bodesheim, Alexander Freytag, Erik Rodner, Joachim Denzler
2015 2015 IEEE Winter Conference on Applications of Computer Vision  
With our local novelty detection approach, we achieve state-of-the-art performance in multi-class novelty detection on two popular visual object recognition datasets, Caltech-256 and ImageNet.  ...  class detection in fine-grained recognition of bird species on the challenging CUB-200-2011 dataset.  ...  However, we have not found any work about local learning for multi-class novelty detection. Multi-class novelty detection Multi-class novelty detection is poorly studied so far.  ... 
doi:10.1109/wacv.2015.113 dblp:conf/wacv/BodesheimFRD15 fatcat:luckbbi4gndirirjutddazqmsy

Improving Gaussian Process Classification with Outlier Detection, with Applications in Image Classification [chapter]

Yan Gao, Yiqun Li
2011 Lecture Notes in Computer Science  
In many computer vision applications for recognition or classification, outlier detection plays an important role as it affects the accuracy and reliability of the result.  ...  Experimental results on handwritten digit image recognition and vision based robot localization show that our approach performs better than other state of the art approaches.  ...  Although a few papers have discussed the multi-class problem in outlier detection, the objectives and problem scope are quite different.  ... 
doi:10.1007/978-3-642-19282-1_13 fatcat:eq6qdnrlzrbq7l3pfwftrhmia4

Visual Attention-Guided Approach to Monitoring of Medication Dispensing Using Multi-location Feature Saliency Patterns

Roman Palenichka, Ahmed Lakhssassi, Myroslav Palenichka
2015 2015 IEEE International Conference on Computer Vision Workshop (ICCVW)  
The attention operator combines a spatial saliency filter with a temporal change (novelty) detector in order to robustly detect salient and object-relevant feature points.  ...  The algorithmic basis of the system is the attentive vision approach to robust and fast object detection in images.  ...  The detection is implemented as a sequential search for local maxima of a multi-scale attention operator.  ... 
doi:10.1109/iccvw.2015.67 dblp:conf/iccvw/PalenichkaLP15 fatcat:tkm27ehnnbdjdlolkkg3kztx6i

Online Open World Recognition [article]

Rocco De Rosa, Thomas Mensink, Barbara Caputo
2016 arXiv   pre-print
We conclude that local and online learning is important to capture the full dynamics of open world recognition.  ...  Recent attempts, like the open world recognition framework, tried to inject dynamics into the system by detecting new unknown classes and adding them incrementally, while at the same time continuously  ...  Still, all these methods estimate the used metric, and the threshold for novelty detection, on an initial closed set of classes, and keep the metric and threshold fixed as the problem evolves.  ... 
arXiv:1604.02275v1 fatcat:nepamsrsjzcinfttfyguzyb4ia

Deep Transfer Learning for Multiple Class Novelty Detection

Pramuditha Perera, Vishal M. Patel
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
We propose a transfer learning-based solution for the problem of multiple class novelty detection.  ...  of a deep network for visual novelty detection.  ...  On the other hand, novelty detection problem has a close resemblance to both anomaly detection [17] , [19] , [20] , [18] , [5] , [16] , and open-set recognition problems [22] , [2] , [7] , [15  ... 
doi:10.1109/cvpr.2019.01181 dblp:conf/cvpr/PereraP19 fatcat:kg76eltkjfgdrao62h2sx3rngm

Automatic identification of novel bacteria using Raman spectroscopy and Gaussian processes

Michael Kemmler, Erik Rodner, Petra Rösch, Jürgen Popp, Joachim Denzler
2013 Analytica Chimica Acta  
Our work aims to tackle this problem of novelty detection using a recently proposed approach based on Gaussian processes.  ...  However, the resulting recognition systems cannot always be directly used in practice since unseen samples might not belong to classes present in the training set.  ...  As in [46] , the multi-class problem was tackled in one-vs-all fashion using a binary GP classifier with Laplace approximation and cumulative Gaussian likelihood.  ... 
doi:10.1016/j.aca.2013.07.051 pmid:23972972 fatcat:dcd2iipxljew5enywwzswoybcy

Robust modular ARTMAP for multi-class shape recognition

Chue Poh Tan, Chen Change Loy, Weng Kin Lai, Chee Peng Lim
2008 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)  
This paper presents a Fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as Modular Adaptive Resonance Theory Map (MARTMAP).  ...  Experimental results show that MARTMAP is able to retain effective familiarity discrimination in noisy environment, and yet less sensitive to class imbalance problem as compared to its counterparts.  ...  solving a multi-class problem as compared with two-class problem by using the same amount of data, since multi-class classification involves an ensemble of several binary classifiers or more complex optimization  ... 
doi:10.1109/ijcnn.2008.4634132 dblp:conf/ijcnn/TanLLL08 fatcat:rh4ozi6di5hkfjprjkityc7ibq

Generalized Out-of-Distribution Detection: A Survey [article]

Jingkang Yang, Kaiyang Zhou, Yixuan Li, Ziwei Liu
2021 arXiv   pre-print
These include anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD).  ...  Meanwhile, several other problems are closely related to OOD detection in terms of motivation and methodology.  ...  Fig. 3 : 3 Timeline for representative methodologies of (a) anomaly detection & one-class novelty detection, details in Section 3, (b) multi-class novelty detection & open set recognition, details in Section  ... 
arXiv:2110.11334v1 fatcat:bfx67gnn6zcr5emwcrfzxs4tom

Deep Transfer Learning for Multiple Class Novelty Detection [article]

Pramuditha Perera, Vishal M. Patel
2019 arXiv   pre-print
We propose a transfer learning-based solution for the problem of multiple class novelty detection.  ...  of a deep network for visual novelty detection.  ...  We presented an end-to-end deep learning-based solution for image novelty detection.  ... 
arXiv:1903.02196v1 fatcat:d7kwj3tv4jggnjkywpjaph2fiq

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild [article]

Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, Fei Sha
2017 arXiv   pre-print
In this paper, we advocate studying the problem of generalized zero-shot learning (GZSL) where the test data's class memberships are unconstrained.  ...  Zero-shot learning (ZSL) methods have been studied in the unrealistic setting where test data are assumed to come from unseen classes only.  ...  But, does this problem setting truly reflect what recognition in the wild entails?  ... 
arXiv:1605.04253v2 fatcat:rkj4ecbhzrfkpn6ojqvvg67xzy

Combining Classifiers for Foreign Pattern Rejection

Władysław Homenda, Agnieszka Jastrzȩbska, Witold Pedrycz, Fusheng Yu
2020 Journal of Artificial Intelligence and Soft Computing Research  
The methods are illustrated with a case study of handwritten digits recognition, but the proposed approach itself is formulated in a general manner.  ...  Therefore, it can be applied to different problems.  ...  Another noteworthy novelty detection method, the so-called Local Outlier Factor, has been presented in [14] .  ... 
doi:10.2478/jaiscr-2020-0006 fatcat:gubboogvjncpdckpqjqy6w3upu

Novelty detection: a review—part 1: statistical approaches

Markos Markou, Sameer Singh
2003 Signal Processing  
In this paper we provide stateof-the-art review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks.  ...  As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.  ...  Conclusion In this paper we have presented a survey of novelty detection using statistical approaches.  ... 
doi:10.1016/j.sigpro.2003.07.018 fatcat:7lbpn2wrrnfprim3bd7ah45i5u

Visual re-identification across large, distributed camera networks

Vildana Sulić Kenk, Rok Mandeljc, Stanislav Kovačič, Matej Kristan, Melita Hajdinjak, Janez Perš
2015 Image and Vision Computing  
We treat the problem of re-identification as an open-world problem, and address novelty detection and forgetting.  ...  We model the re-identification process in a distributed camera network as a distributed multi-class classifier, composed of spatially distributed binary classifiers.  ...  Recognition performance Since we deal with multi-class classification, we first evaluate multiclass recognition performance as seen from the recipient of the multiple distances scores (4) .  ... 
doi:10.1016/j.imavis.2014.11.002 fatcat:csfsmkbpw5bgrlyeul2jaj6uqm

Hack The Box: Fooling Deep Learning Abstraction-Based Monitors [article]

Sara Hajj Ibrahim, Mohamed Nassar
2021 arXiv   pre-print
Correctly invalidating the prediction of unrelated classes is a challenging problem that has been tackled in many ways in the literature.  ...  Novelty detection gives deep learning the ability to output "do not know" for novel/unseen classes. Still, no attention has been given to the security aspects of novelty detection.  ...  A similar approach based on learning a local model around a test sample is pro-posed on [Bodesheim et al., 2015] for Multi-class novelty detection tasks in image recognition problems.  ... 
arXiv:2107.04764v3 fatcat:sgojuv5bhja6zfjmarl5hprdf4

A literature review on one-class classification and its potential applications in big data

Naeem Seliya, Azadeh Abdollah Zadeh, Taghi M. Khoshgoftaar
2021 Journal of Big Data  
Commonly used techniques in OCC for outlier detection and for novelty detection, respectively, are discussed.  ...  We observed one area that has been largely omitted in OCC-related literature is its application context for big data and its inherently associated problems, such as severe class imbalance, class rarity  ...  Acknowledgements We would like to thank the various reviewers in the Data Mining and Machine Learning Laboratory at Florida Atlantic University, Boca Raton, FL 33431.  ... 
doi:10.1186/s40537-021-00514-x fatcat:iaqfshjii5butmn64yrecd5yxq
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