CAJ | 학술논문

基于视词字典树的算法由于高效性使其在基于大规模图像数据库的目标检索领域得到了广泛地应用。该类算法属于从文字搜索领域借鉴来的“视觉词袋”的算法。这种算法中的一个关键步骤是将高维特征向量量化成视词。将这种量化过程看作高维特征向量的最近邻搜索问题，并且提出一种随机维哈希（RDH）算法用于索引视词字典。实验结果证明，该算法比基于字典树的算法具有更高的量化精度，从而可以显著提高目标检索性能。
기우시사자전수적산법유우고효성사기재기우대규모도상수거고적목표검색영역득도료엄범지응용。해류산법속우종문자수색영역차감래적“시각사대”적산법。저충산법중적일개관건보취시장고유특정향량양화성시사。장저충양화과정간작고유특정향량적최근린수색문제，병차제출일충수궤유합희（RDH）산법용우색인시사자전。실험결과증명，해산법비기우자전수적산법구유경고적양화정도，종이가이현저제고목표검색성능。
Visual-words dictionary tree-based algorithm has been widely applied in object retrieval in large-scale image database due to its efficiency.Such algorithm appertains to the bag-of-visual-words algorithm which is borrowed from text search field.A key step of such algo-rithm is to quantify the high-dimensional feature vectors to the visual words.In this paper,we consider the quantification process as the nea-rest neighbour search of high-dimensional feature vectors,and propose a randomised dimensions hashing algorithm to index the visual-word dictionary.Experimental results demonstrate that the proposed algorithm has higher quantification accuracy than the vocabulary tree-based al-gorithms,thus it can significantly improve object retrieval performance.