CAJ | 학술논문

针对传统的GVP(Geometry-Preserving Visual Phrases)图像检索算法计算量大、时间复杂度高且不适合处理大规模图像检索等缺点，文章提出了FSF-GVP(Frequency Statistics Feature-Geometry-Preserving Visual Phrases)算法，该方法将词频统计特征和GVP算法相结合，使用GVP排序算法对词频特征统计后的相似结果集进行排序，忽略不相似结果集，极大地提高了检索效率。实验结果表明，FSF-GVP在保证检索准确性的前提下，提高了检索效率，适用于实时大规模图像检索。
침대전통적GVP(Geometry-Preserving Visual Phrases)도상검색산법계산량대、시간복잡도고차불괄합처리대규모도상검색등결점，문장제출료FSF-GVP(Frequency Statistics Feature-Geometry-Preserving Visual Phrases)산법，해방법장사빈통계특정화GVP산법상결합，사용GVP배서산법대사빈특정통계후적상사결과집진행배서，홀략불상사결과집，겁대지제고료검색효솔。실험결과표명，FSF-GVP재보증검색준학성적전제하，제고료검색효솔，괄용우실시대규모도상검색。
Traditional GVP (geometry-preserving visual phrases) image retrieval algorithm is not suitable for handling the large-scale image retrieval because of its high time complexity. In this paper, FSF-GVP (frequency statistics feature-geometry-preserving visual phrases) algorithm, which combined word frequency statistic characteristics and GVP algorithm, was proposed. FSF-GVP algorithm counts visual word frequency characteristics of an image to be searched and image database to get similar result set and dissimilar result set. Then FSF-GVP algorithm uses the GVP algorithm to sort the similar result set, which improves the retrieval efifciency. The experiment results on Oxford 5K dataset show that FSF-GVP is suitable for the large-scale real-time image retrieval on the premise of ensuring the accuracy of retrieving result and improving the retrieval efifciency.