To address the problem that the sentiment annotation of paintings requires a significant cost,a sentiment clustering method for abstract paintings was designed.A color feature representation and a texture feature representation,both based on three-way decisions,were proposed along with an enhanced deep learning model.These representations were utilized for extrac-ting color,texture,and high-level semantic features from abstract painting images.Subsequently,the three features were adap-tively fused using a multi-kernel k-means algorithm,resulting in sentiment clustering outcomes for the images.Experimental results show that compared with four benchmark methods on the MART and Deviant Art datasets,this method improves the ac-curacy,Fowlkes-Mallows index,and normalized mutual information by an average of 30,23,and 49 percentage points,respec-tively.The method performs well in the application of sentiment clustering analysis of abstract paintings,which also provides a benchmark for unsupervised sentiment analysis studies of other art paintings.
维普
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