IKNN-SVM: A Hybrid Incremental Algorithm for Image Classification

DOI

10.2991/aiie-16.2016.55How to use a DOI?

Keywords

component; support vector machine; kd-tree; k nearest neighbors; image classification; incremental learning

Abstract

Incremental learning is a machine learning paradigm that the training dataset grows dynamically at runtime and the prediction accuracy can be improved incrementally. Since real-life datasets are usually open-ended and dynamic, incremental learning is important to many image classification applications. In existing incremental algorithms, KNN (K Nearest Neighbors) has the advantage of dealing with the hugely and incrementally multiclass nature of visual object recognition. However, it provides good prediction only when the query image is similar to one of the images in the dataset, which results in low accuracy in many cases. In this paper, we propose a hybrid incremental learning algorithm named IKNN-SVM which can improve the recognition accuracy while reserving the advantages of KNN, i.e., quickly learning from the increasing number of data. To achieve this goal, our incremental algorithm combines SVM (Support Vector Machine) with high recognition accuracy and KNN together. And to promote the incremental learning efficiency, IKNN-SVM introduce kd-tree to implement fast searching. The experimental result based on the open dataset Caltech256 shows that the recognition accuracy of our method improves by 12%-26% compared with incremental KNN but with short time consumption.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Huimin Che
AU  - Bo Ding
AU  - Huaimin Wang
AU  - Ben Hu
AU  - Huifang Che
PY  - 2016/11
DA  - 2016/11
TI  - IKNN-SVM: A Hybrid Incremental Algorithm for Image Classification
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 235
EP  - 239
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.55
DO  - 10.2991/aiie-16.2016.55
ID  - Che2016/11
ER  -