Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding
Abstract
Image understanding is an important research domain in the computer vision due to its wide real-world applications. For an image understanding framework that uses the Bag-of-Words model representation, the visual codebook is an essential part. Random forest (RF) as a tree-structure discriminative codebook has been a popular choice. However, the performance of the RF can be degraded if the local patch labels are poorly assigned. In this paper, we tackle this problem by a novel way to update the RF codebook learning for a more discriminative codebook with the introduction of the soft class labels, estimated from the pLSA model based on a feedback scheme. The feedback scheme is performed on both the image and patch levels respectively, which is in contrast to the state- of-the-art RF codebook learning that focused on either image or patch level only. Experiments on 15-Scene and C-Pascal datasets had shown the effectiveness of the proposed method in image understanding task.
Cite
@article{arxiv.1410.3752,
title = {Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding},
author = {Wai Lam Hoo and Tae-Kyun Kim and Yuru Pei and Chee Seng Chan},
journal= {arXiv preprint arXiv:1410.3752},
year = {2014}
}
Comments
Accepted in ICPR 2014 (Oral)