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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.  ...  In this paper, we propose using local learning for multiclass novelty detection, a framework that we call local novelty detection.  ...  Experimental results for novel visual object detection in object recognition as well as for the specific applications of novel face detection and unknown bird species detection are presented in Sect. 4  ... 
doi:10.1109/wacv.2015.113 dblp:conf/wacv/BodesheimFRD15 fatcat:luckbbi4gndirirjutddazqmsy

Kernel Null Space Methods for Novelty Detection

Paul Bodesheim, Alexander Freytag, Erik Rodner, Michael Kemmler, Joachim Denzler
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
Detecting samples from previously unknown classes is a crucial task in object recognition, especially when dealing with real-world applications where the closed-world assumption does not hold.  ...  Beside the possibility of modeling a single class, we are able to treat multiple known classes jointly and to detect novelties for a set of classes with a single model.  ...  Identifying samples from currently unknown classes is hence an essential step in visual object recognition.  ... 
doi:10.1109/cvpr.2013.433 dblp:conf/cvpr/BodesheimFRKD13 fatcat:ntfashyxvbdx5kif2mzuh73oay

Automatically Discovering Properties That Specify the Latent Behavior of UML Models [chapter]

Heather J. Goldsby, Betty H. C. Cheng
2010 Lecture Notes in Computer Science  
In contrast, this paper proposes an automated approach to generating temporal logic properties that specify the latent behavior of existing UML models; these are unknown properties exhibited by the system  ...  The Marple-discovered properties can be used to refine the models to either remove unwanted behavior or to explicitly document a desirable property as required system behavior.  ...  While in previous work novelty search was used to discover better solutions than other evolutionary computation techniques, in this paper, we use novelty search to discover a suite of properties that cumulatively  ... 
doi:10.1007/978-3-642-16145-2_22 fatcat:bd5vampdjjdndikpwa763lliku

Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks

Marco Antonio Contreras-Cruz, Juan Pablo Ramirez-Paredes, Uriel Haile Hernandez-Belmonte, Victor Ayala-Ramirez
2019 Sensors  
The proposed framework presented high-novelty detection accuracy with competitive or even better results than the baseline methods.  ...  In this work, we propose a visual novelty detection framework for specific exploration and inspection tasks based on evolved novelty detectors.  ...  In general, the novelty detection methods construct a model with the examples of the normal class and use this model with unknown data to compute novelties.  ... 
doi:10.3390/s19132965 fatcat:srzvv426mbfbzjxypxgej3dusm

Digits that are not: Generating new types through deep neural nets [article]

Akın Kazakçıand Mehdi Cherti, Balázs Kégl
2016 arXiv   pre-print
propose a notion of knowledge-driven creativity that circumvent the need for an externally imposed value function, allowing the system to explore based on what it has learned from a set of referential objects  ...  For an artificial creative agent, an essential driver of the search for novelty is a value function which is often provided by the system designer or users.  ...  x), or novelty (or outlier) detection (where the goal is to detect objects from a stream which do not look like objects in D by thresholding p(x)).  ... 
arXiv:1606.04345v1 fatcat:7rg3nbqyarbsrldl6uvwloatim

Visualizing Image Content to Explain Novel Image Discovery [article]

Jake H. Lee, Kiri L. Wagstaff
2019 arXiv   pre-print
We focus on the goal of detecting observations with novel content, which can alert us to artifacts in the data set or, potentially, the discovery of previously unknown phenomena.  ...  To aid in interpreting and diagnosing the novel aspect of these selected observations, we recommend the use of novelty detection methods that generate explanations.  ...  Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.  ... 
arXiv:1908.05006v1 fatcat:flaadp62fjc47a2strwtv5tjo4

Fractal Analogies for General Intelligence [chapter]

Keith McGreggor, Ashok Goel
2012 Lecture Notes in Computer Science  
In the second, we describe how the fractal technique enables the percept-to-action mapping in a simple, simulated world.  ...  In the first part, we describe a technique of fractal analogies and show how it gives human-level performance on an intelligence test called the Odd One Out.  ...  Markou & Singh [20] [21] review statistical and neural network techniques for novelty detection. Neto & Nehmzow [24] illustrate the use of visual novelty detection in autonomous robots.  ... 
doi:10.1007/978-3-642-35506-6_19 fatcat:ukklrgokjrhafaikx4s6635obm

Exploration strategies for incremental learning of object-based visual saliency

Celine Craye, David Filliat, Jean-Francois Goudou
2015 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)  
In order to get a better and faster learning, we use intrinsic motivation to drive our observation selection, based on uncertainty and novelty detection.  ...  Searching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available.  ...  The selection of target is often obtained with a saliency map, but also by novelty detection [12] , reinforcement learning techniques [15] , or in very simple configuration, competence progress [1]  ... 
doi:10.1109/devlrn.2015.7346099 dblp:conf/icdl-epirob/CrayeFG15 fatcat:qchxd22e3bfqdkqb2i7i7o56bq

A technique to detect periodic and non-periodic ultra-rapid flux time variations with standard radio-astronomical data

Ermanno F. Borra, Jonathan D. Romney, Eric Trottier
2018 Journal of astrophysics and astronomy  
Another advantage is that it allows a direct visualization of the shape of the signals, while it is difficult to see the shape with a Fourier transform.  ...  Another major novelty of our work is that we use electric fields taken in a standard format with standard instrumentation at a radio observatory and therefore no specialized instrumentation is needed.  ...  Since then,33 objects with The autocorrelation will not be advantageous over conventional matched-filtering techniques for detecting periodic repeating bursts, particularly if these conventional techniques  ... 
doi:10.1007/s12036-018-9525-6 fatcat:23ebsnosdbbazjgsy7sebmtuby

Motivation Learning in Mind Model CAM

Zhongzhi Shi, Gang Ma, Xi Yang, Chengxiang Lu
2015 International Journal of Intelligence Science  
Introspective search can perform novelty detection to discover novel events.  ...  and unknown object information during testing.  ... 
doi:10.4236/ijis.2015.52006 fatcat:nklrdhakjba7dbmehienri2cc4

An Analysis of Outlier Detection through clustering method

T. Chandrakala, S. Nirmala Sugirtha Rajini
2020 International Journal of Advanced engineering Management and Science  
This research paper deals with an outlier which is known as an unusual behavior of any substance present in the spot.  ...  This is a detection process that can be employed for both anomaly detection and abnormal observation. This can be obtained through other members who belong to that data set.  ...  INTRODUCTION Mining, in general, is termed as the intrinsic methodology of discovering interesting, formerly unknown data patterns.  ... 
doi:10.22161/ijaems.612.13 fatcat:w7c55r4y2fdvrndhx7lelwrtaa

Vision based, statistical learning system for fault recognition in industrial assembly environment

First Zs. J. Viharos, Second D. Chetverikov, Third A. Hary, Fourth R. Saghegyi, Fifth A. Barta, Sixth L. Zalanyi, Seventh I. Pomozi, Eighth Sz. Soos, Ninth Zs. Kover, Tenth B. Varju
2016 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)  
The paper presents a statistical learning system based visual solution developed and applied for fault detection in industrial environment.  ...  The developed system is able to detect faults with the size of 2% of the total picture based on previously learned models.  ...  tasks: it has to detect the object from motion information, collect frame pictures from it, separate the object from the background, find the statistically most corresponding model with comparing the object  ... 
doi:10.1109/etfa.2016.7733730 dblp:conf/etfa/ViharosCHSBZPSK16 fatcat:nfqkmtka4nfe7ohrvads543ikm

Novelty detection: a review—part 1: statistical approaches

Markos Markou, Sameer Singh
2003 Signal Processing  
Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training.  ...  Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time  ...  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

Analyzing change in spatial data by utilizing polygon models

Vadeerat Rinsurongkawong, Chun Sheng Chen, Christoph F. Eick, Michael D. Twa
2010 Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application - COM.Geo '10  
We evaluate our framework in case studies that center on ozone pollution monitoring, and on diagnosing glaucoma from visual field analysis.  ...  In this paper, we present a framework for the detection and analysis of patterns of change; the framework analyzes change by comparing sets of polygons.  ...  In contrast, our work is applicable for data with unknown object identity and for datasets that contain numerical attributes.  ... 
doi:10.1145/1823854.1823880 dblp:conf/comgeo/RinsurongkawongCET10 fatcat:zdtza4rbp5bghojqnarmacfoti

Data Mining in Education : A Review on the Knowledge Discovery Perspective

Pratiyush Guleria, Manu Sood
2014 International Journal of Data Mining & Knowledge Management Process  
In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data mining, its techniques and methods in it.  ...  patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with  ...  data mining tools are DBMiner, Clementine, Intelligent Miner, RapidMiner and Weka etc [11] .DM combines machine learning, statistics and visualization techniques to discover and extract knowledge.  ... 
doi:10.5121/ijdkp.2014.4504 fatcat:5wldcmrngvgojhnwbb5nafze4a
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