Hashing has recently received great attention in cross-modal retrieval. Cross-modal retrieval
aims at retrieving information across heterogeneous modalities (eg, texts vs. images). Cross …

Convolutional neural networks have been effective in several applications, arising as a
promising supporting tool in a relevant Dermatology problem: skin cancer diagnosis …

Given that retrieval and storage have compelling efficiency, similarity-preserving hashing
has been extensively employed to approximate nearest neighbor search in large-scale …

Supervised linear hashing can compress high-dimensional data into compact binary codes
owing to its efficiency. Generally, the relation between label and hash codes is widely used …

To overcome the barrier of storage and computation when dealing with gigantic-scale data
sets, compact hashing has been studied extensively to approximate the nearest neighbor …

Local feature points have been widely employed in robust image fingerprinting. One of their
intrinsic advantages is their invariance under geometric transforms. However, their …

Efficient hashing techniques have attracted extensive research interests in both storage and
retrieval of high-dimensional data, such as images and videos. In existing hashing methods …

As binary code is storage efficient and fast to compute, it has become a trend to compact real-
valued data to binary codes for the nearest neighbors (NN) search in a large-scale …

Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data
relationship. It is critical in many machine learning, pattern recognition and data mining …

Approximate Nearest Neighbor (ANN) search is the core problem in many large-scale
machine learning and computer vision applications such as multimodal retrieval. Hashing is …