Salient Local Feature Extraction Algorithm Based on Integrated Visual Attention Model
Zuqiao Yang, Cehn Yuepeng, Qing Zhang · 2013
Local features are widely used for content-based image retrieval recently.During image retrieval,a lot of local features are extracted,which increases the amount of calculation and complexity of image retrieval,and as a result,affecting the practical applications.With an eye towards this problem,a novel method based on integrated visual attention model was proposed to extract salient local features.Using this method,first,the key points in an image scale-space are extracted,and the salient area of the original image is found using fuzzy growth technology,then the integrated visual saliency is calculated and classified,and SIFT factors are extracted and ranked according to their integrated visual saliency,and at last,only the most distinctive features are kept to enhance the retrieval performance.The experimental results demonstrate that compared to traditional local feature extraction algorithms,this salient local feature extraction algorithm based on integrated visual attention model provides significant benefits both in retrieval accuracy and speed.