*Result*: Intelligent painting identification based on image perception in multimedia enterprise.

*Recent works in image perception and multimedia technology have paved the way for automated analysis of visual arts. Intelligent painting based on image perception, processing and identification in multimedia enterprise is the trend. In this paper, we use a Cross- Contrast Neural Network (CCNN) model to automatic art identification in oil painting. To achieve this point, we first retrained a tailored CNN network to extract features of oil paintings and then calculated the cross-contrast probability map utilising the contrast information to measure the similarity between input images. We aim to combine IBS with CNN to facilitate the training progress and reduce the difficulty of finetuning the parameters of CNN. To demonstrate the effectiveness of our approach, we introduced the Selected-Wikipaintings dataset, containing over 5000 images painted by 20 artists in various styles. The average accuracy of the classification task in 20 artists achieved 85.75%, which proved to be more accurate than the general method in artists identification task. Our extensive experimental evaluation shows that CCNN owns better classification performance in small data-set multi-classification scenes. [ABSTRACT FROM AUTHOR]

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