Intelligent Classification of Multimedia Images Based on Class Information Mining

Shuai Xiao, Xiaotong Shen, Guipeng Lan, Jiachen Yang, Jiabao Wen, Yong Zhu

Abstract: The classification research of multimedia images has always been of great concern, and related technologies are constantly being improved to increase accuracy. Adequate evaluation and mining of sample information is an important direction, but it is always a challenge. In this article, we propose a method for constructing deep learning training datasets, which fully considers the intra class and inter class features of the samples. The intra class dispersion of the sample is evaluated by the distance from the sample features to the prototype, while inter class confusion between classes is evaluated by combining the distance of the prototype in the feature space with intra class dispersion. Based on the intra class and inter class features of samples, determine the proportion of imbalanced construction to achieve the construction of imbalanced datasets. This method has the potential to be applied to different multimedia visual tasks.