Emotional modelling and classification of a large-scale collection of scene images in a cluster environment

• Published in: PloS one Vol. 13; no. 1; p. e0191064
• Main Authors: Cao, Jianfang, Li, Yanfei, Tian, Yun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.01.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Big Data; Biology and Life Sciences; Classification; Clusters; Collection; Computer and Information Sciences; Computer science; Computers; Digital imaging; Emotions; Engineering and Technology; Image classification; Image processing equipment; Image retrieval; Learning; Modelling; Mood; Parallel programming; Personality; Physical Sciences; Research and Analysis Methods; Researchers; Semantics; Social Sciences; Studies; Support vector machines; Technology; Training; Wavelet transforms
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0191064

The development of network technology and the popularization of image capturing devices have led to a rapid increase in the number of digital images available, and it is becoming increasingly difficult to identify a desired image from among the massive number of possible images. Images usually contain rich semantic information, and people usually understand images at a high semantic level. Therefore, achieving the ability to use advanced technology to identify the emotional semantics contained in images to enable emotional semantic image classification remains an urgent issue in various industries. To this end, this study proposes an improved OCC emotion model that integrates personality and mood factors for emotional modelling to describe the emotional semantic information contained in an image. The proposed classification system integrates the k-Nearest Neighbour (KNN) algorithm with the Support Vector Machine (SVM) algorithm. The MapReduce parallel programming model was used to adapt the KNN-SVM algorithm for parallel implementation in the Hadoop cluster environment, thereby achieving emotional semantic understanding for the classification of a massive collection of images. For training and testing, 70,000 scene images were randomly selected from the SUN Database. The experimental results indicate that users with different personalities show overall consistency in their emotional understanding of the same image. For a training sample size of 50,000, the classification accuracies for different emotional categories targeted at users with different personalities were approximately 95%, and the training time was only 1/5 of that required for the corresponding algorithm with a single-node architecture. Furthermore, the speedup of the system also showed a linearly increasing tendency. Thus, the experiments achieved a good classification effect and can lay a foundation for classification in terms of additional types of emotional image semantics, thereby demonstrating the practical significance of the proposed model.