Abstract
As an information carrier with rich semantics, images contain more sentiment than texts and audios. So, images are increasingly used by people to express their opinions and sentiments in social network. The sentiments of the images are overall and should come from different regions. So, the recognition of the sentiment regions will help to concentrate on important factors the affect the sentiments. Meanwhile, deep learning method for image sentiment classification needs simple and efficient approach for simultaneously carrying out pruning and feature selection whilst optimizing the weights. Motivated by these observations, we design a region-based convolutional neural network using group sparse regularization for image sentiment classification: R-CNNGSR. The method obtains the initial sentiment prediction model through CNN using group sparse regularization to get compact neural network, and then automatically detect the sentiment regions by combining the underlying features and sentimental features. Finally, the whole image and the sentiment region are fused to predict the overall sentiment of the images. Experiment results demonstrate that our proposed R-CNNGSR significantly outperforms the state-of-the-art methods in image sentiment classification.
Highlights
The background of modern information technology has achieved explosive development and application
IAPS subset is derived from the International Affective Picture System called IAPS [30], which is a common sentiment data set that is widely used in sentiment classification research
5.2 Results and analysis 5.2.1 On image sentiment classification In order to verify the superiority of R-CNNGSR proposed in this paper, we compare R-CNNGSR with four image sentiment classification methods in experiments, which are VGGNet, fine-tuning VGGNet, progressive CNN (PCNN), and ARconcatenation
Summary
The background of modern information technology has achieved explosive development and application. In traditional image sentiment classification, an image is in general associated with one or more sentiment labels, which belong to categorical models [11] In these methods, extracting features are the most important component for classification performance. VGGNet and fine-tuned VGGNet with 16 layers or 19 layers have been widely used for extracting deep features of images and improve the classification performance of image sentiment [7, 8, 18] These deep features are the high-level features including more semantic information which is good at emotion. PCNN progressively selected a subset of the training instances to reduce the impact of noisy training instances and got prominent improvement [19] These works did not consider the important effect of image regions with rich sentiments for classification. The framework highly depends on target detection method EdgeBoxes which is an object recognition method [20] and not suitable for sentiment region detection
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