Automatic textile image annotation by predicting emotional concepts from visual features

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摘要

This paper presents an emotion prediction system that can automatically predict certain human emotional concepts from a given textile. The main application motivating this study is textile image annotation, which has recently rapidly expanded in relation to the Web. In the proposed method, color and pattern are used as cues to predict the emotional semantics associated with an image, where these features are extracted using a color quantization and a multi-level wavelet transform, respectively. The extracted features are then applied to three representative classifiers: K-means clustering, Naïve Bayesian, and a multi-layered perceptron (MLP), all of which are widely used in data mining. When evaluating the proposed emotion prediction method using 3600 textile images, the MLP produces the best performance. Thereafter, the proposed MLP-based method is compared with other methods that only use color or pattern, and the proposed method shows the best performance with an accuracy of above 92%. Therefore, the results confirm that the proposed method can be effectively applied to the commercial textile industry and image retrieval.

论文关键词:Automatic image annotation,Affective features,Emotion recognition,Textile image retrieval: neural network

论文评审过程:Received 20 May 2009, Revised 1 August 2009, Accepted 25 August 2009, Available online 9 September 2009.

论文官网地址:https://doi.org/10.1016/j.imavis.2009.08.009