Xie Yuxiang

Utilizing Locality-Sensitive Hash Learning for Cross-Media Retrieval

Cross-media retrieval is an imperative approach to handle the explosive growth of multimodal data on the web. However, existed approaches to cross-media retrieval are computationally expensive due to the curse of dimensionality. To efficiently retrieve in multimodal data, it is essential to reduce the proportion of irrelevant documents. In this paper, we propose a cross-media retrieval approach (FCMR) based on locality-sensitive hashing (LSH) and neural networks. Multimodal information is projected by LSH algorithm to cluster similar objects into the same hash bucket and dissimilar objects into different ones, using hash functions learned through neural networks. Once given a textual or visual query, it can be efficiently mapped to a hash bucket in which objects stored can be near neighbors of this query. Experimental results show that, in the set of the queries’ near neighbors obtained by the proposed method, the proportions of relevant documents can be much boosted, and it indicates that the retrieval based on near neighbors can be effectively conducted. Further evaluations on two public datasets demonstrate the effectiveness of the proposed retrieval method compared to the baselines.

An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

Aiming at the problem that the image similarity detection efficiency is low based on local feature, an algorithm called ScSIFT for image similarity acceleration detection based on sparse coding is proposed. The algorithm improves the image similarity matching speed by sparse coding and indexing the extracted local features. Firstly, the SIFT feature of the image is extracted as a training sample to complete the overcomplete dictionary, and a set of overcomplete bases is obtained. The SIFT feature vector of the image is sparse-coded with the overcomplete dictionary, and the sparse feature vector is used to build an index. The image similarity detection result is obtained by comparing the sparse coefficients. The experimental results show that the proposed algorithm can significantly improve the detection speed compared with the traditional algorithm based on local feature detection under the premise of guaranteeing the accuracy of algorithm detection.