Abstract
The development of multimedia technology and the popularisation of image capture devices have resulted in the rapid growth of digital images. The reliance on advanced technology to extract and automatically classify the emotional semantics implicit in images has become a critical problem. We proposed an emotional semantic classification method for images based on the Adaboost-backpropagation (BP) neural network, using natural scenery images as examples. We described image emotions using the Ortony, Clore, and Collins emotion model and constructed a strong classifier by integrating 15 outputs of a BP neural network based on the Adaboost algorithm. The objective of the study was to improve the efficiency of emotional image classification. Using 600 natural scenery images downloaded from the Baidu photo channel to train and test the model, our experiments achieved results superior to the results obtained using the BP neural network method. The accuracy rate increased by approximately 15% compared with the method previously reported in the literature. The proposed method provides a foundation for the development of additional automatic sentiment image classification methods and demonstrates practical value.
Highlights
With the recent development of multimedia technology and the rapid popularisation of various image capture devices, the scale of digital imagery is expanding rapidly
A semantic gap refers to the distance between low-level and high-level retrieval needs due to inconsistency between the image visual information acquired by the computer and the image semantic information understood by the user
We developed an emotion semantic classification system for natural scenery images using Matlab to perform the training and test processes
Summary
With the recent development of multimedia technology and the rapid popularisation of various image capture devices, the scale of digital imagery is expanding rapidly. CBIR technology only retrieves images from a database on the basis of low-level visual features (colour, texture, and shape), which are not the users’ desired retrieval requirements. The emotional semantic classification of images as a newly emerging technique has become an intense research topic This technique is capable of retrieving image-emotion semantic adjectives, extracting connotative semantic information, and reflecting human affection towards the image, which constitutes an essential part of the study of digital image comprehension. These characteristics will enable an examination of multimedia information retrieval and the filtering and interception of illegal information
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