Ten lectures on wavelets
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Image classification and querying using composite region templates
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concurrent Self-Organizing Maps for Pattern Classification
ICCI '02 Proceedings of the 1st IEEE International Conference on Cognitive Informatics
Histogram refinement for content-based image retrieval
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Genetic Evolution Processing of Classification
IEEE Transactions on Knowledge and Data Engineering
Image coding by adaptive tree-structured segmentation
IEEE Transactions on Information Theory
Image compression using binary space partitioning trees
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
Multilayer SOM with tree-structured data for efficient document retrieval and plagiarism detection
IEEE Transactions on Neural Networks
Tree structures with attentive objects for image classification using a neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Image classification with saliency region and multi-task learning
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Multi-class learning from class proportions
Neurocomputing
Applying a lightweight iterative merging chinese segmentation in web image annotation
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Image classification is a challenging problem of computer vision. Conventional image classification methods use flat image features with fixed dimensions, which are extracted from a whole image. Such features are computationally effective but are crude representation of the image content. This paper proposes a new image classification approach through a tree-structured feature set. In this approach, the image content is organized in a two-level tree, where the root node at the top level represents the whole image and the child nodes at the bottom level represent the homogeneous regions of the image. The tree-structured representation combines both the global and the local features through the root and the child nodes. The tree-structured feature data are then processed by a two-level self-organizing map (SOM), which consists of an unsupervised SOM for processing image regions and a supervising concurrent SOM (CSOM) classifier for the overall classification of images. The proposed method incorporates both global image features and local region-based features to improve the performance of image classification. Experimental results show that this approach performs better than conventional approaches.