Edge detection is one of the most important and fundamental problems in the field of
computer vision and image processing. Edge contours extracted from images are widely …

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 …

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 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 this paper, we present a new hashing method to learn compact binary codes for highly
efficient image retrieval on large-scale datasets. While the complex image appearance …

Benchmark datasets are critical for developing, evaluating, and comparing remote sensing
image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in …

Target encoding plays a central role when learning Convolutional Neural Networks. In this
realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so …

With a widespread use of digital imaging data in hospitals, the size of medical image
repositories is increasing rapidly. This causes difficulty in managing and querying these …

Recent years have witnessed wide application of hashing for large-scale image retrieval.
However, most existing hashing methods are based on hand-crafted features which might …

Wafer maps provide important information for engineers in identifying root causes of die
failures during semiconductor manufacturing processes. We present a method for wafer map …