An automatic hierarchical image classification scheme
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining confident co-location rules without a support threshold
Proceedings of the 2003 ACM symposium on Applied computing
Fast mining of spatial collocations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
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
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Mining spatial association rules in image databases
Information Sciences: an International Journal
From frequent itemsets to semantically meaningful visual patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Direct Discriminative Pattern Mining for Effective Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Mining frequent spatial patterns in image databases
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Image classification for content-based indexing
IEEE Transactions on Image Processing
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Effective use of frequent itemset mining for image classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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Owing to the rapid mounting of massive image data, image classification has attracted lots of research efforts. Several diverse research disciplines have been confluent on this important theme, looking for more powerful solutions. In this paper, we propose a novel image representation method B2S (Bag to Set) that keeps all frequency information and is more discriminative than traditional histogram based bag representation. Based on B2S, we construct two different image classification approaches. First, we apply B2S to a state-of-the-art image classification algorithm SPM in computer vision. Second, we design a framework DisIClass (Discriminative Frequent Pattern-Based Image Classification) to utilize data mining algorithms to classify images, which was hardly done before due to the intrinsic differences between the data of computer vision and data mining fields. DisIClass adapts the locality property of image data, and apply sequential covering method to induce the most discriminative feature sets from a closed frequent item set mining method. Our experiments with real image data show the high accuracy and good scalability of both approaches.