Locality-sensitive hashing

About: Locality-sensitive hashing is a research topic. Over the lifetime, 1894 publications have been published within this topic receiving 69362 citations.

Deep learning based supervised hashing for efficient image retrieval

Abstract: Due to its storage and search efficiency, hashing has attracted great attentions in large-scale vision problems such as image retrieval and recognition. This paper presents a novel Deep Learning based Supervised Hashing (DLSH) method by using a deep neural network to better capture the semantic structure of nonlinear and complex data. We consider learning a nonlinear embedding that simultaneously preserves semantic information and produces nearby binary codes for semantically similar data. Specifically, our hashing model is trained to maximize the similarity measure of neighbor pairs while preserving the relative similarity of non-neighbor pairs with a relaxed empirical penalty in the binary space. An effective regularizer for minimizing the quantization loss between the learned embedding and the binary codes is also considered in the optimization to generate better hash code quality. Experimental results have demonstrated the proposed method outperforms the state-of-the-art methods.

A convolutional treelets binary feature approach to fast keypoint recognition

Abstract: Fast keypoint recognition is essential to many vision tasks. In contrast to the classification-based approaches [1,2], we directly formulate the keypoint recognition as an image patch retrieval problem, which enjoys the merit of finding the matched keypoint and its pose simultaneously. A novel convolutional treelets approach is proposed to effectively extract the binary features from the patches. A corresponding sub-signature-based locality sensitive hashing scheme is employed for the fast approximate nearest neighbor search in patch retrieval. Experiments on both synthetic data and real-world images have shown that our method performs better than state-of-the-art descriptor-based and classification-based approaches.

A scalable algorithm for monte carlo localization using an incremental E 2 LSH-database of high dimensional features

Abstract: In recent years, high-dimensional descriptive features have been widely used for feature-based robot localization. However, the space/time costs of building/retrieving the map database tend to be significant due to the high dimensionality. In addition, most of existing databases are working well only on batch problems, difficult to be built incrementally by a mapper robot. In this paper, a scalable localization algorithm is proposed for incremental databases of high dimensional features. The Monte Carlo localization (MCL) algorithm is extended by employing the exact Euclidean locality sensitive hashing (LSH). The robustness and efficiency of the proposed algorithms have been demonstrated using the radish dataset.

Multi-feature locality sensitive hashing (LSH) indexing combination-based remote sensing image retrieval method

Abstract: The invention discloses a multi-feature locality sensitive hashing (LSH) indexing combination-based remote sensing image retrieval method and belongs to the technical field of remote sensing image retrieval. According to the multi-feature LSH indexing combination-based remote sensing image retrieval method disclosed by the invention, LSH indexing of one of the best indexing technologies in high-dimensional feature spaces is introduced into the field of the remote sensing image retrieval, so that the problems of curse of dimensionality and retrieval time consuming can be effectively solved on a large scale, and the rapid retrieval of remote sensing images is realized. Meanwhile, the invention provides a new indexing validation index-a feature discriminative-ness-based indexing validation index (FDIVI) by aiming at the LSH indexing, and features best capable of distinguishing targets and backgrounds are evaluated and selected by the LSH indexing in all feature spaces, and therefore, the accuracy of a retrieval result is effectively improved. Compared with the prior art, the multi-feature LSH indexing combination-based remote sensing image retrieval method disclosed by the invention is capable of more rapidly and accurately realizing the retrieval of a great amount of remote sensing image data.