Research On Hashing-Based Image Retrieval For Large-Scale Dataset

In Web2.0time, with the popularity of camera, mobile phone and Pad, people can take photos anytime and upload them to social websites. According to the research,1.8ZB data has been created and duplicated in2011,75%of which is unstructured data including images, videos, and music files. Dealing with such large-scale data, how to quickly and accurately obtain valuable information is essential for data management. When using traditional Nearest Neighbor search methods to deal with large-scale image retrieval, it results in large memory cost and low retrieval speed owing to the curse of dimensionality.Image hashing method uses hash functions to encode high-dimensional data into low-dimens binary hash codes, preserving the similarity of high-dimensional data. Due to the compact fea-ture representation, small storage cost, and high retrieval speed, image hashing has been widely applied to Content-Based Image Retrieval (CBIR). Focused on image hashing, this dissertation introduces image retrieval in large-scale image databases. The content of this dissertation is as following:(1) To deal with images without labels, this dissertation proposes an unsupervised multi-table weakly principal component hashing method. Firstly, for each hash table, projecting data to different weakly principal directions to get input data for each hash function. Secondly, or-thogonal rotation is adopted to rotate projection directions, optimize projection matrix of hash functions, and improve distinction among data. Experiments on two international acknowledged databases have proved the efficiency of proposed method. Compared with6existing hashing methods, the proposed hashing method has been effectively tested on CIFAR10and MNIST databases.(2) When retrieving in a database with some labeled images, this dissertation proposes a supervised Boosting-based image hashing method from the perspective of feature selection. Ac-cording to the classification result by previous hash table, the weights for input samples of current hash table and input samples for each hash table are determined. In addition, to preserve sample relationships and minimize quantization error, projected hashing vector is optimized. Compared with7existing hashing methods, the proposed hashing method has been effectively tested on CIFAR10and MNIST databases. (3) To solve the ordering problem of returned images caused by integer value of Hamming distance, this dissertation proposes two hashing-based image reranking methods, including dis-tance weights based reranking method (DWR) and bit discrimination based reranking method (BPIR). DWR method averages Euclidean distance of equal hash bits to these bits with differ-ent values, so as to obtain the weights of hash codes. By comparing the discrimination between query image and returned images via Hamming distance, BPIR method assigns higher weights to discriminative bits and lower weights to non-discriminative bits. CIFAR10and MNIST database are used to prove the effectiveness and necessity of proposed two reranking methods.