510 research outputs found
Unsupervised Visual Hashing with Semantic Assistant for Content-Based Image Retrieval
As an emerging technology to support scalable content-based image retrieval (CBIR), hashing has recently received great attention and became a very active research domain. In this study, we propose a novel unsupervised visual hashing approach called semantic-assisted visual hashing (SAVH). Distinguished from semi-supervised and supervised visual hashing, its core idea is to effectively extract the rich semantics latently embedded in auxiliary texts of images to boost the effectiveness of visual hashing without any explicit semantic labels. To achieve the target, a unified unsupervised framework is developed to learn hash codes by simultaneously preserving visual similarities of images, integrating the semantic assistance from auxiliary texts on modeling high-order relationships of inter-images, and characterizing the correlations between images and shared topics. Our performance study on three publicly available image collections: Wiki, MIR Flickr, and NUS-WIDE indicates that SAVH can achieve superior performance over several state-of-the-art techniques
A deep locality-sensitive hashing approach for achieving optimal ‎image retrieval satisfaction
Efficient methods that enable high and rapid image retrieval are continuously needed, especially with the large mass of images that are generated from different sectors and domains like business, communication media, and entertainment. Recently, deep neural networks are extensively proved higher-performing models compared to other traditional models. Besides, combining hashing methods with a deep learning architecture improves the image retrieval time and accuracy. In this paper, we propose a novel image retrieval method that employs locality-sensitive hashing with convolutional neural networks (CNN) to extract different types of features from different model layers. The aim of this hybrid framework is focusing on both the high-level information that provides semantic content and the low-level information that provides visual content of the images. Hash tables are constructed from the extracted features and trained to achieve fast image retrieval. To verify the effectiveness of the proposed framework, a variety of experiments and computational performance analysis are carried out on the CIFRA-10 and NUS-WIDE datasets. The experimental results show that the proposed method surpasses most existing hash-based image retrieval methods
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search
Mobile landmark search (MLS) recently receives increasing attention for its
great practical values. However, it still remains unsolved due to two important
challenges. One is high bandwidth consumption of query transmission, and the
other is the huge visual variations of query images sent from mobile devices.
In this paper, we propose a novel hashing scheme, named as canonical view based
discrete multi-modal hashing (CV-DMH), to handle these problems via a novel
three-stage learning procedure. First, a submodular function is designed to
measure visual representativeness and redundancy of a view set. With it,
canonical views, which capture key visual appearances of landmark with limited
redundancy, are efficiently discovered with an iterative mining strategy.
Second, multi-modal sparse coding is applied to transform visual features from
multiple modalities into an intermediate representation. It can robustly and
adaptively characterize visual contents of varied landmark images with certain
canonical views. Finally, compact binary codes are learned on intermediate
representation within a tailored discrete binary embedding model which
preserves visual relations of images measured with canonical views and removes
the involved noises. In this part, we develop a new augmented Lagrangian
multiplier (ALM) based optimization method to directly solve the discrete
binary codes. We can not only explicitly deal with the discrete constraint, but
also consider the bit-uncorrelated constraint and balance constraint together.
Experiments on real world landmark datasets demonstrate the superior
performance of CV-DMH over several state-of-the-art methods
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