CM-supplement network model for reducing the memory consumption during multilabel image annotation

With the rapid development of the Internet and the increasing popularity of mobile devices, the availability of digital image resources is increasing exponentially. How to rapidly and effectively retrieve and organize image information has been a hot issue that urgently must be solved. In the field of image retrieval, image auto-annotation remains a basic and challenging task. Targeting the drawbacks of the low accuracy rate and high memory resource consumption of current multilabel annotation methods, this study proposed a CM-supplement network model. This model combines the merits of cavity convolutions, Inception modules and a supplement network. The replacement of common convolutions with cavity convolutions enlarged the receptive field without increasing the number of parameters. The incorporation of Inception modules enables the model to extract image features at different scales with less memory consumption than before. The adoption of the supplement network enables the model to obtain the negative features of images. After 100 training iterations on the PASCAL VOC 2012 dataset, the proposed model achieved an overall annotation accuracy rate of 94.5%, which increased by 10.0 and 1.1 percentage points compared with the traditional convolution neural network (CNN) and double-channel CNN (DCCNN). After stabilization, this model achieved an accuracy of up to 96.4%. Moreover, the number of parameters in the DCCNN was more than 1.5 times that of the CM-supplement network. Without increasing the amount of memory resources consumed, the proposed CM-supplement network can achieve comparable or even better annotation effects than a DCCNN.