International Journal of Computer Vision
C4.5: programs for machine learning
C4.5: programs for machine learning
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Shape quantization and recognition with randomized trees
Neural Computation
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Randomized K-Dimensional Binary Search Trees
ISAAC '98 Proceedings of the 9th International Symposium on Algorithms and Computation
Environment-specific novelty detection
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The Journal of Machine Learning Research
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Support Vector Data Description
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Hierarchical Part-Based Visual Object Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Image Segmentation by Networks of Spiking Neurons
Neural Computation
FOCUS: a generalized method for object discovery for robots that observe and interact with humans
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Machine Learning
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Learning Top-Down Grouping of Compositional Hierarchies for Recognition
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time Automated Visual Inspection using Mobile Robots
Journal of Intelligent and Robotic Systems
Visual novelty detection with automatic scale selection
Robotics and Autonomous Systems
Multidimensional Binary Search Trees in Database Applications
IEEE Transactions on Software Engineering
Exploration of configural representation in landmark learning using working memory toolkit
Pattern Recognition Letters
A Divide-and-Conquer Approach for Minimum Spanning Tree-Based Clustering
IEEE Transactions on Knowledge and Data Engineering
Multilayer pLSA for multimodal image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Domain-guided novelty detection for autonomous exploration
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A computational neuroscience model of working memory with application to robot perceptual learning
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Unsupervised Object Discovery: A Comparison
International Journal of Computer Vision
International Journal of Robotics Research
Fast support vector data descriptions for novelty detection
IEEE Transactions on Neural Networks
Machine Vision and Applications
Tracking-based semi-supervised learning
International Journal of Robotics Research
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It is generally agreed that learning, either supervised or unsupervised, can provide the best possible specification of known classes and offer inference for outlier detection by a dissimilarity threshold from the nominal feature space. Novel percept detection can take a step further by investigating whether these outliers form new dense clusters in both the feature space and the image space. By defining a novel percept to be a pattern group that has not been seen before in the feature space and the image space, in this paper, a non-conventional approach is proposed for multiple-novel-percept detection problem in robotic applications. Based on a computer vision system inspired loosely by neurobiological evidence, our approach can work in near real time for highly sparse high-dimensional feature vectors extracted from image patches while maintaining robustness to image transformations. Experiments conducted in an indoor environment and an outdoor environment demonstrate the efficacy of our method.