Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

During the long history of computer vision, one of the grand challenges has been semantic
segmentation which is the ability to segment an unknown image into different parts and …

Satellite images taken on the earth's surface are analyzed to identify the spatial and
temporal changes that have occurred naturally or manmade. Real-time prediction of change …

Face recognition performance evaluation has traditionally focused on one-to-one
verification, popularized by the Labeled Faces in the Wild data set [1] for imagery and the …

Real-time video objects detection, tracking, and recognition are challenging issues due to
the real-time processing requirements of the machine learning algorithms. In recent years …

For large-scale visual search, highly compressed yet meaningful representations of images
are essential. Structured vector quantizers based on product quantization and its variants …

The objective of this work is to recognize object categories in paintings, such as cars, cows
and cathedrals. We achieve this by training classifiers from natural images of the objects. We …

Semantic gap is an important challenging problem in content-based image retrieval (CBIR)
up to now. Bag-of-words (BOW) framework is a popular approach that tries to reduce the …

With the recent penetration and proliferation of social networks into our lives, human choices
and preferences have become more socially accessible. This easy accessibility of private …

The semantic video indexing problem is still underexplored. Solutions to the problem will
significantly enrich the experience of video search, monitoring, and surveillance. This paper …