Feature hashing

About: Feature hashing is a research topic. Over the lifetime, 993 publications have been published within this topic receiving 51462 citations.

Deep Asymmetric Pairwise Hashing

Abstract: Recently, deep neural networks based hashing methods have greatly improved the multimedia retrieval performance by simultaneously learning feature representations and binary hash functions. Inspired by the latest advance in the asymmetric hashing scheme, in this work, we propose a novel Deep Asymmetric Pairwise Hashing approach (DAPH) for supervised hashing. The core idea is that two deep convolutional models are jointly trained such that their output codes for a pair of images can well reveal the similarity indicated by their semantic labels. A pairwise loss is elaborately designed to preserve the pairwise similarities between images as well as incorporating the independence and balance hash code learning criteria. By taking advantage of the flexibility of asymmetric hash functions, we devise an efficient alternating algorithm to optimize the asymmetric deep hash functions and high-quality binary code jointly. Experiments on three image benchmarks show that DAPH achieves the state-of-the-art performance on large-scale image retrieval.

Method and apparatus for indexing document content and content comparison with World Wide Web search service

Abstract: Methods and related systems for indexing the contents of documents for comparison with the contents of other documents to identify matching content. A method for comparing the contents of a query document to the content on the World Wide Web is set forth. The contents of a query document are indexed and compared to content from the World Wide Web which is continuously retrieved and indexed. The method for indexing may comprise selecting substrings from the document, hashing the substrings to generate a plurality of hash values having a known range of values, selecting certain hash values to save from the generated hash values, and sorting the saved hash values. Methods for selecting certain hash values to save are set forth.

Deep Video Hashing

Abstract: In this work, we propose a deep video hashing (DVH) method for scalable video search. Unlike most existing video hashing methods that first extract features for each single frame and then use conventional image hashing techniques, our DVH learns binary codes for the entire video with a deep learning framework so that both the temporal and discriminative information can be well exploited. Specifically, we fuse the temporal information across different frames within each video to learn the feature representation under two criteria: the distance between a feature pair obtained at the top layer is small if they are from the same class, and large if they are from different classes; and the quantization loss between the real-valued features and the binary codes is minimized. We exploit different deep architectures to utilize spatial-temporal information in different manners and compare them with single-frame-based deep models and state-of-the-art image hashing methods. Experimental results demonstrate the effectiveness of our proposed method.

Dynamic Multi-view Hashing for Online Image Retrieval

Abstract: Advanced hashing technique is essential to facilitate effective large scale online image organization and retrieval, where image contents could be frequently changed. Traditional multi-view hashing methods are developed based on batch-based learning, which leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising performance when searching real online images, which are multiple view based data. Further, both types of hashing methods can only produce hash code with fixed length. Consequently they suffer from limited capability to comprehensive characterization of streaming image data in the real world. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated during the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is also specifically designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.