Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in 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
Multilevel Image Coding with Hyperfeatures
International Journal of Computer Vision
Automated Flower Classification over a Large Number of Classes
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Direct Discriminative Pattern Mining for Effective Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
DisIClass: discriminative frequent pattern-based image classification
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Video mining with frequent itemset configurations
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Mining discriminative co-occurrence patterns for visual recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper we propose a new and effective scheme for applying frequent itemset mining to image classification tasks. We refer to the new set of obtained patterns as Frequent Local Histograms or FLHs. During the construction of the FLHs, we pay special attention to keep all the local histogram information during the mining process and to select the most relevant reduced set of FLH patterns for classification. The careful choice of the visual primitives and some proposed extensions to exploit other visual cues such as colour or global spatial information allow us to build powerful bag-of-FLH-based image representations. We show that these bag-of-FLHs are more discriminative than traditional bag-of-words and yield state-of-the art results on various image classification benchmarks.