A Novel Image Classification Algorithm Based on Word Bag Model and Feature Extraction
- 10.2991/amcce-17.2017.68How to use a DOI?
- Image classification, feature extraction, Bag of Words, SIFT descriptor, SVM classifier.
Image classification is based on the different characteristics of the image information to distinguish the images into different categories. It makes quantitative analysis of the image, interpreting the image or the image of each pixel region into a plurality of categories or in one, to replace the human visual interpretation. Traditional classification methods are usually based on single feature analysis. In this paper, a novel algorithm based on word bag model is described. This algorithm extracts a variety of image features at the same time, then establishes comprehensive access to multiple feature vectors, and finally gets more accurate image classification results. Bag of words model is a generalization of the traditional bag of words model. The core idea of the bag of words model method is that: all the words in the corpus statistics composed of words, words for each document statistics on the frequency of use, composed of the words frequency histogram to express this document. Corresponding to this model, we present the texture feature extraction and analysis steps.
- © 2017, 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/).
Cite this article
TY - CONF AU - Zikang Tang AU - Hao Zhang AU - FanLu Zhang PY - 2017/03 DA - 2017/03 TI - A Novel Image Classification Algorithm Based on Word Bag Model and Feature Extraction BT - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017) PB - Atlantis Press SP - 386 EP - 393 SN - 2352-5401 UR - https://doi.org/10.2991/amcce-17.2017.68 DO - 10.2991/amcce-17.2017.68 ID - Tang2017/03 ER -