An Object Recognition Method Based on Bag-of-Visual-Words and Fusing Multi-feature
Xueting Qi, Tianhuang Chen, Hongxia Wang
Available Online May 2014.
- https://doi.org/10.2991/lemcs-14.2014.216How to use a DOI?
- object recognition;BOV model;fusing multi-feature;SIFT;LBP
- The traditional bag-of-visual-words(BOV) model only uses one single feature to classify objects, which is difficult to achieve good results when dealing with many object categories. To solve this problem, in this paper, we proposed an object recognition method based on BOV model and fusing multi-feature. First, it extracted Scale Invariant Feature Transform (SIFT) features and Local Binary Pattern(LBP) features in the multi-scale space simultaneously, Second, we utilized SVM classifier to pre-classify separately on these two kinds of features and assigned their weights 0 or 1 according to the scale of pre-classification results. Third, the method fused SIFT and LBP features by introducing their weights, obtaining a fused vector. At last, classified on the fused vector using SVM classifier again, and then achieved the final result of object recognition. The experimental results show that the method proposed in this paper shows good performance and could improve the accuracy of object recognition effectively.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xueting Qi AU - Tianhuang Chen AU - Hongxia Wang PY - 2014/05 DA - 2014/05 TI - An Object Recognition Method Based on Bag-of-Visual-Words and Fusing Multi-feature BT - International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2014) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-14.2014.216 DO - https://doi.org/10.2991/lemcs-14.2014.216 ID - Qi2014/05 ER -