Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

In the early days, content-based image retrieval (CBIR) was studied with global features.
Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …

Hashing is an efficient method for nearest neighbor search in large-scale data space by
embedding high-dimensional feature descriptors into a similarity preserving Hamming …

Quantization based techniques are the current state-of-the-art for scaling maximum inner
product search to massive databases. Traditional approaches to quantization aim to …

Hashing methods for efficient image retrieval aim at learning hash functions that map similar
images to semantically correlated binary codes in the Hamming space with similarity well …

In recent years, deep neural networks have emerged as a dominant machine learning tool
for a wide variety of application domains. However, training a deep neural network requires …

Image retrieval can be formulated as a ranking problem where the goal is to order database
images by decreasing similarity to the query. Recent deep models for image retrieval have …

Similarity search finds application in database systems handling complex data such as
images or videos, which are typically represented by high-dimensional features and require …