关键词: 多特征融合, 深度学习, 受限玻尔兹曼机, 图像标注

Abstract: To bridge the semantic gap between low-level visual feature and high-level semantic concepts has been the subject of intensive investigation on big data management for years, in order to improve the accuracy of image auto-annotation. Multi-features fusion for image auto-annotation based on DBN model is proposed, combining image visual features with different weights as inputs of DBN model and optimize parameters of DBN, achieving image automatic annotation on big image data. The experimental results based on Corel image database show that the proposed method, taking different features of images into considering, has good performance on annotation precision.

Key words: multi-features fusion, deep learning, restricted Boltzmann machine, image annotation