Title :
An Improved Bilinear Deep Belief Network Algorithm for Image Classification
Author :
Niu Jie ; Bu Xiongzhu ; Li Zhong ; Wang Yao
Author_Institution :
Sch. of Mech. Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China
Abstract :
A novel image recognition method based on the improved BDBN (Bilinear Deep Belief Network) model is presented, optimized with a MKL (Multiple Kernel Learning) strategy. All kernel functions in MKL are replaced by hierarchical feature representations, and the number of kernels is set to the number of layers of BDBN. The method is performed on the standard Caltech101 image dataset. The experiments show that the proposed method can improve the accuracy of traditional BDBN methods by up to 2.8%, and the accuracy of the method is superior to some methods in the literature.
Keywords :
belief networks; image classification; image representation; learning (artificial intelligence); visual databases; BDBN model; MKL strategy; bilinear deep belief network algorithm; hierarchical feature representations; image classification; image recognition method; multiple kernel learning strategy; standard Caltech101 image dataset; Accuracy; Classification algorithms; Computer vision; Educational institutions; Feature extraction; Image classification; Kernel; BDBN; Multiple Kernel Learning; deep learning; image classification;
Conference_Titel :
Computational Intelligence and Security (CIS), 2014 Tenth International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4799-7433-7
DOI :
10.1109/CIS.2014.38
