视觉词语的产生是基于字袋模型的图像检索中的重要一环:根据已知的视觉词典,查询图像特征被映射到词典中相应的视觉词语。提出一种新的基于空间相关性的快速视觉词语产生算法。统计视觉词典中任意两个词语在数据库中的共生次数,构建视觉词语共生表。利用共生表,建立一种新的概率预测器来辅助预测已知词语的近邻词语。将预测器与快速近似最近邻查找算法结合,在标准图像检索数据库上进行实验测试,相比较传统的树形搜索算法或哈希算法,新算法在时间效率上获得明显提高。

Visual word generation is a key observation in obtaining the bag-of-visual-words (BOVW)representation for image retrieval: query image features are mapped to their visual words according to the pre-clustered codebook. In this paper, we propose a novel generation approach based on the spatial correlation of visual words. A visual word co-occurrence table is constructed in the first step. Given the known visual words, a new probabilistic predictor is then presented to acce- lerate the generation of their neighboring visual words. We combine the co-occurrence table with the fast library for approximate nearest neighbors (FLANN), and test it on the Oxford dataset. Comparisons with representative approaches suggest the efficiency and effectiveness of the new scheme.