Hierarchical Learning for Large-Scale Image Classification via CNN and Maximum Confidence Path

We propose a framework to integrate the large scale image data visualization with image classification. The Convolution Neural Network is used to learn the feature vector for an image. A fast algorithm is developed for inter-class similarity measurement. The spectral clustering is implemented to construct a hierarchical visual tree. Instead of the flat classification way, a hierarchical classification is designed according to the visual tree, which is transformed to a path search problem. The path with the maximum joint probability is the final solution. Experimental results on the ILSVRC2010 dataset demonstrate that our method achieves the highest top-1 and top-5 classification accuracy in comparison with 6 state-of-the-art methods.

[1]  Antonio Criminisi,et al.  Object categorization by learned universal visual dictionary , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[2]  Jianping Fan,et al.  Jointly Learning Visually Correlated Dictionaries for Large-Scale Visual Recognition Applications , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Jianping Fan,et al.  Mining Multilevel Image Semantics via Hierarchical Classification , 2008, IEEE Transactions on Multimedia.

[4]  Alexei A. Efros,et al.  Unsupervised discovery of visual object class hierarchies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Jianping Fan,et al.  Quantitative Characterization of Semantic Gaps for Learning Complexity Estimation and Inference Model Selection , 2012, IEEE Transactions on Multimedia.

[6]  Quanfu Fan,et al.  Robust Spatiotemporal Matching of Electronic Slides to Presentation Videos , 2011, IEEE Transactions on Image Processing.

[7]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[8]  Alexander C. Berg,et al.  Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition , 2011, NIPS.

[9]  Jianping Fan,et al.  Structured Max-Margin Learning for Inter-Related Classifier Training and Multilabel Image Annotation , 2011, IEEE Transactions on Image Processing.

[10]  Pietro Perona,et al.  Learning and using taxonomies for fast visual categorization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[12]  Florent Perronnin,et al.  High-dimensional signature compression for large-scale image classification , 2011, CVPR 2011.

[13]  Pietro Perona,et al.  Unsupervised learning of visual taxonomies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Jason Weston,et al.  Label Embedding Trees for Large Multi-Class Tasks , 2010, NIPS.

[15]  Frédéric Jurie,et al.  Sampling Strategies for Bag-of-Features Image Classification , 2006, ECCV.

[16]  Cordelia Schmid,et al.  Constructing Category Hierarchies for Visual Recognition , 2008, ECCV.

[17]  Ming Yang,et al.  Large-scale image classification: Fast feature extraction and SVM training , 2011, CVPR 2011.

[18]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[19]  Cordelia Schmid,et al.  Towards good practice in large-scale learning for image classification , 2012, CVPR.

[20]  Frédéric Jurie,et al.  Fast Discriminative Visual Codebooks using Randomized Clustering Forests , 2006, NIPS.

[21]  Jianping Fan,et al.  Cost-sensitive learning of hierarchical tree classifiers for large-scale image classification and novel category detection , 2015, Pattern Recognit..