Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Newsjunkie: providing personalized newsfeeds via analysis of information novelty
Proceedings of the 13th international conference on World Wide Web
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Neural Network-Based Novelty Detector for Image Sequence Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining color and spatial information for object recognition across illumination changes
Pattern Recognition Letters
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
An information-pattern-based approach to novelty detection
Information Processing and Management: an International Journal
One-Class Classification by Combining Density and Class Probability Estimation
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
ACM Computing Surveys (CSUR)
On the use of co-occurrence matrices for network anomaly detection
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
IEEE Transactions on Circuits and Systems for Video Technology
Wildlife video key-frame extraction based on novelty detection in semantic context
Multimedia Tools and Applications
One-class classification with Gaussian processes
Pattern Recognition
Review: A review of novelty detection
Signal Processing
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Novelty detection is an important functionality that has found many applications in information retrieval and processing. In this paper we propose a novel framework that deals with novelty detection in multiple-scene image sets. Working with wildlife image data, the framework starts with image segmentation, followed by feature extraction and classification of the image blocks extracted from image segments. The labelled image blocks are then scanned through to generate a co-occurrence matrix of object labels, representing the semantic context within the scene. The semantic co-occurrence matrices then undergo binarization and principal component analysis for dimension reduction, forming the basis for constructing one-class models on scene categories. An algorithm for outliers detection that employs multiple one-class models is proposed. An advantage of our approach is that it can be used for novelty detection and scene classification at the same time. Our experiments show that the proposed approach algorithm gives favourable performance for the task of detecting novel wildlife scenes, and binarization of the semantic co-occurrence matrices helps increase the robustness to variations of scene statistics.