The problem of object retrieval is often addressed with the BoVW (bag of visual words) method. There are several problems in the traditional BoVW such as low time efficiency and large memory consumption,and synonymy and polysemy of visual words. In this paper, an object retrieval method based on exact Euclideanlocality sensitive hashing (E2LSH) is proposed. E2LSH is used to hash local features of the training dataset,and a group of scalable random visual vocabularies is constructed. Then, the visual vocabulary histograms and index files are created according to these random vocabularies. The term frequency vectors are weighted with tf-idf strategy. Similarity matching between histogram of the query object and index files is made to accomplish object retrieval. Experimental results show that accuracy of the proposed method is substantially improved compared to the traditional methods. The method is applicable to large scale datasets.