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- Title
Tree-CNN: from generalization to specialization.
- Authors
Jiang, Shenwang; Xu, Tingfa; Guo, Jie; Zhang, Jizhou
- Abstract
Traditional convolutional neural networks (CNNs) classify all categories by a single network, which passes all kinds of samples through totally the same network flow. In fact, it is quite challengeable to distinguish schooner with ketch and chair by a single network. To address it, we propose a new image classification architecture composed of a cluster algorithm and the Tree-CNN. The cluster algorithm devotes to classifying similar fine categories into a coarse category. The Tree-CNN is comprised of a Trunk-CNN for coarse classification of all categories and Branch-CNNs to treat different groups of similar categories differently. Branch-CNNs are fine-tuning based on the Trunk-CNN, which extracts the special feature map of image and divides it into fine categories. But Branch-CNNs bring extra computation and are hard to train. To address it, we introduce adaptive algorithm to balance the heavy computation and accuracy. We have tested Tree-CNNs based on CaffeNet, VGG16, and GoogLeNet in Caltech101 and Caltech256 for image classification. Experiment results show the superiority of the proposed Tree-CNN.
- Subjects
ARTIFICIAL neural networks; GENERALIZATION; SIGNAL convolution; ALGORITHMS; IMAGE processing
- Publication
EURASIP Journal on Wireless Communications & Networking, 2018, Vol 2018, Issue 1, p1
- ISSN
1687-1472
- Publication type
Article
- DOI
10.1186/s13638-018-1197-z