Image Recognition Based on Separable Lattice Hmms Using a Deep Neural Network for Output Probability Distributions
Eiji Ichikawa, Kei Sawada, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda
Abstract
This paper proposes an image recognition method based on separable lattice hidden Markov models (SLHMMs) using a deep neural network (DNN) for output probability distributions. The geometric variations of the object to be recognized, e.g., size and location, are essential in image recognition. SLHMMs, which have been proposed to reduce the effect of geometric variations, can perform elastic matching both horizontally and vertically. Gaussian distributions are typical for modeling the output distribution of SLHMMs. However, these distributions may not be sufficient to represent patterns of image regions. Our method integrates SLHMMs and a DNN and can be used to model an image effectively by explicit modeling of the generative process based on SLHMMs and advanced feature classification based on a DNN. image recognition experiments showed that the proposed method improves recognition performance.
BibTeX
@inproceedings{icassp2018_imagerecognition,
title = {Image Recognition Based on Separable Lattice Hmms Using a Deep Neural Network for Output Probability Distributions},
author = {Eiji Ichikawa and Kei Sawada and Kei Hashimoto and Yoshihiko Nankaku and Keiichi Tokuda},
booktitle = {ICASSP 2018},
year = {2018}
}