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• A novel debiased contrastive hashing method for accurate image retrieval. • Introducing neighborhood discovery to alleviate the bias problem caused by false negatives. • We bridge contrastive hashing and the EM algorithm to provide some insights. • Our proposed method outperforms state-of-the-art unsupervised hashing methods. Hashing has achieved great success in multimedia retrieval due to its high computing efficiency and low storage cost. Recently, contrastive-learning-based hashing methods have achieved decent retrieval performance in label-free scenarios by learning distortion-invariant representations with Siamese networks. Their learning principle, i.e., instance discrimination , maximizes the correlation between self-augmented views and treats all others as negative samples. However, it may learn with false negative samples that are naturally similar, resulting in biased hash learning. To bridge this flaw, we reveal the between-instance similarity of naturally similar samples by exploring the latent structure of the training data. As a result, we propose the D eep D ebiased C ontrastive H ashing (DDCH) algorithm, using the neighborhood discovery module to explore the intrinsic similarity relationship that can help contrastive hashing reduce false negatives for superior discriminatory ability. Furthermore, we elucidate the rationale for incorporating the module into the contrastive hashing framework and explain our hashing process from an Expectation-Maximization (EM) perspective. Extensive experimental results on three benchmark image datasets demonstrate that DDCH significantly outperforms the state-of-the-art unsupervised hashing methods for image retrieval. [ABSTRACT FROM AUTHOR]

Infrared imaging-based machine vision (IRMV) is the technology used to automatically inspect, detect, and analyse infrared images (or videos) obtained by recording the intensity of infrared light emitted or reflected by observed objects. Depending on whether controllable excitation is used during the imaging of infrared rays, thermal IRMV can be categorised into passive thermography and active thermography. Passive thermography is an important supplement to conventional machine vision based on visible light and is a valid imaging tool for self-heating objects such as the human body and electrical power devices. Active thermography is a non-destructive testing method for the quality evaluation and safety assurance of non-self-heating objects. In active thermography, the trend is to inspect rapidly, reliably, and intelligently by introducing multiple-mode excitation sources and artificial intelligence. The rapid development of deep learning makes IRMV more and more intelligent and highly automated, thus considerably increasing its range of applications. This paper reviews the principle, cameras, and thermal data of IRMV and discusses the applications of deep learning applied to IRMV. Case studies of IRMV and deep learning on various platforms such as unmanned vehicles, mobile phones and embedded systems are also reported. [ABSTRACT FROM AUTHOR]