Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent images as off-the-shelf visual features and then produce hashing codes in a separate stage. However, off-the-shelf visual features may not be optimally compatible with the hash code learning procedure, which may result i...

In recent years, deep hashing methods have been proved to be efficient since it employs convolutional neural network to learn features and hashing codes simultaneously. However, these methods are mostly supervised. In real-world application, it is a time-consuming and overloaded task for annotating a large number of images. In this paper, we propose a novel unsupervised deep hashing method for ...

With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefiting from recent advances in deep learning, deep hashing methods have achieved promising results for image retrieval. However, there are some limitations of previous deep hashing methods (e.g., the semantic information is not fully exploited). In this p...

Similarity-based image hashing represents crucial technique for visual data storage reduction and expedited image search. Conventional hashing schemes typically feed handcrafted features into hash functions, which separates the procedures of feature extraction and hash function learning. In this paper, we propose a novel algorithm that concurrently performs feature engineering and non-linear su...

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, whi...

Advanced hashing technique is essential in large scale online image retrieval and organization, where image contents are frequently changed. While traditional multi-view hashing method has achieve promising effectiveness, its batch-based learning based scheme largely leads to very expensive updating cost. Meanwhile, existing online hashing scheme generally focuses on single-view data. Good effe...

We address the problem of image hashing by learning binary codes from large and weakly supervised photo collections. Due to the explosive growth of usergenerated media on the Web, this problem is becoming critical for large-scale visual applications like image retrieval. While most existing hashing methods fail to address this challenge well, our method shows promising improvement due to the fo...

Detection of image forgery is always a crucial factor in image forensic and security applications. Usually this detection is possible with the help of local or global features of an image. We can ensure the credibility of an image with a hashing method by fusing local and global features together. So that it is possible to detect even sensitive image forgeries. Here, we are proposing an improve...