Content-Driven Associative Memories for Color Image Patterns
提出一种内容驱动联想记忆模型，通过三层递归神经张量网络关联大规模彩色图像，能抵抗相关噪声并保持空间结构，适用于图像内容识别与检索。
This paper presents a novel content-driven associative memory (CDAM) to associate large-scale color images based on the subjects that represent the images' content. Compared to traditional associative memories, CDAM inherits their tolerance to random noise in images and possesses greater robustness against correlated noise that distorts an image's spatial contextual structure. A three-layer recurrent neural tensor network (RNTN) is designed as the network model of CDAM. Multiple salient objects detection algorithm and partial radial basis function (PRBF) kernel are proposed for subject determination and content-driven association, respectively. Convergence of the RNTN is analyzed based on the properties of PRBF kernels. Extensive comparative experiment results are provided to verify the CDAM's efficiency, robustness, and accuracy.