Concerning the problem that the Deep Symbolic Regression （DSR） algorithm， which generates expression trees through Recurrent Neural Network （RNN） automatically， cannot ensure both accuracy and structural simplicity simultaneously， a Double decision mechanism-based DSR （DDSR） algorithm was proposed. Firstly， a dual scoring mechanism was employed to evaluate the accuracy and simplicity of the expression trees comprehensively on the basis of initial RNN decision. Then， reinforcement learning was used to train the expression trees， and Risk Proximal Policy Optimization （RPPO） algorithm was utilized to perform reward feedback， so as to update model parameters of the next batch. Experimental results on public datasets show that compared with DSR algorithm， DDSR algorithm achieves a maximum improvement of 0.396 and a minimum improvement of 0.001 in the coefficient related to fitness， with an average gain of 0.116. The above proves the effectiveness of DDSR algorithm.

In order to improve the defection performance of MEPG-7 Dominant Color Descriptor (DCD) that it is prone to lose the spatial information of colors, in this paper, blocks weighted dominant color descriptor was used, as well as the correlation feedback method was carried out. It used correlation feedback method to adjust weight value of the block and the dominant color feature in the block. Experimental results show that the method is much more effective than those based on only dominant colors and blocks dominant colors without feedback.