Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Large margin hierarchical classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
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
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Bilinear deep learning for image classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Can the World-Wide Web Bridge the Semantic Gap?
Image and Vision Computing
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In the field of machine learning and pattern recognition, an alternative to conventional classification is hierarchical classification that exploits hierarchical relations between concepts of interest. To the best of our knowledge, all hierarchical classification methods in the literature are designed to reduce computation complexity without sacrificing too much on accuracy performance. In this work on image classification, we first propose a hierarchical image feature extraction that extracts image feature based on the location of current node in hierarchy to fit the images under current node and to better distinguish its subclasses. As far as we know, such node-dependent feature extraction has not been considered in the literature. Contrary to former hierarchical classification methods that only consider local structure of the hierarchy, we propose a novel cross-level hierarchical classification method that utilizes both global and local concept structures throughout the entire path decision-making process. Our experimental result on two datasets shows that the proposed hierarchical feature extraction combined with our novel hierarchical classification achieves better accuracy performance than conventional non-hierarchical classification methods, and hence conventional hierarchical methods as well.