A Novel Image Retrieval Approach with Bag-of-Word Model and Gabor Feature
摘要:
In the past few years, image retrieval has been one of the hot spots in computer vision field. Among many image retrieval techniques, Bag-of-Word (BoW) model is one of the effective and efficient methods that can search images with visual vocabularies and it is insensitive to massive data and various geometric attacks. But the classical BoW algorithm used some descriptors as its visual words, such as SIFT, which are also used to build visual vocabulary. The main problem with traditional BoW algorithm is that the visual vocabulary could not reflect the spatial information of visual words. And most BoW algorithms utilize one single feature as their feature vector, some other important features are ignored. All these factors have influenced the accuracy of final result in traditional BoW model. In order to solve the problem, we propose a novel image retrieval method with BoW and Gabor feature.The paper first proposes a new saliency map extraction method, and then the saliency score is used to build visual vocabulary.At last, Gabor feature is combined with visual vocabulary together to compute similarity. In order to test the effectiveness of the algorithm, we evaluate our method on the SIMPLICITY dataset and Stanford dataset. Experiments results demonstrate the effectiveness and accuracy of the proposed method.
展开
关键词:
Visualization Vocabulary Image color analysis Image retrieval Feature extraction Histograms Image segmentation
DOI:
10.1109/TrustCom.2016.0261
年份:
2016
通过文献互助平台发起求助,成功后即可免费获取论文全文。
相似文献
参考文献
引证文献
辅助模式
引用
文献可以批量引用啦~
欢迎点我试用!