12 May 2018 Topic network: topic model with deep learning for image classification
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Abstract
As a representative deep learning model, convolutional neural networks (CNNs) have accomplished great achievements in image classification and object detection. However, CNNs require the resizing of the input images to a fixed size, which may affect the representations of objects. To overcome this limitation, we replace the last pooling layer with a topic model and call it a topic network. For arbitrary sizes and ratios of input images, the outputs of the topic network are fixed-size features due to the topic model of the topic layer, and they can reflect the global or regional characteristics of images by means of different scales. Two topic models, namely latent Dirichlet allocation (LDA) and Markov topic random fields (MTRF), are applied to the topic layer, and we call them latent Dirichlet allocation topic network and Markov topic random fields topic network, respectively. Both of them perform well in image classification with original size. More importantly, as a framework, any topic model can be easily applied to the topic layer of a topic network, which makes it much more flexible and extensible.
© 2018 SPIE and IS&T
Zhiyong Pan, Zhiyong Pan, Yang Liu, Yang Liu, Guojun Liu, Guojun Liu, Maozu Guo, Maozu Guo, Yang Li, Yang Li, } "Topic network: topic model with deep learning for image classification," Journal of Electronic Imaging 27(3), 033009 (12 May 2018). https://doi.org/10.1117/1.JEI.27.3.033009 . Submission: Received: 12 December 2017; Accepted: 12 April 2018
Received: 12 December 2017; Accepted: 12 April 2018; Published: 12 May 2018
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