Semantic enhanced deep learning for image classification. (13th December 2017)

• Record Type: Journal Article
• Authors: Li, Siguang; Li, Maozhen; Jiang, Changjun
• Other Names: Barbosa Jorge G. guestEditor.; Jeannot Emmanuel guestEditor.; Li Maozhen guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 23(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-12-13
• Subjects: convolutional neural network -- deep learning -- image classification -- semantic gap -- stacked auto‐encoder; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4388
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Extracting training data semantics mainly depends on manual annotation samples prepared in advance, and semantic mapping largely relies on the prior conditions in image classification. A majority of the data samples in reality are unlabeled data, which necessitates a more efficient way to extract image semantics in classification. Deep learning, as an advanced machine learning technique, has recently received significant attention from both the academia and industry. Deep learning has the potential to extract semantic features from images in an automated way to fill up the semantic gap. This paper presents 2 novel deep learning models (ie, stacked denoising auto‐encoder and convolution deep Boltzmann machine). A stacked denoising auto‐encoder builds on a stacked auto‐encoder but uses a denoising auto‐encoder to improve the learning quality. Convolution deep Boltzmann machine combines the convolutional neural network model with the deep Boltzmann machine. These 2 models are evaluated on CIFAR‐10 and STL‐10 datasets, respectively. Experimental results show that the proposed 2 models outperform the original models in terms of both precision and recall.