Texture is a fundamental characteristic of many types of images, and texture representation
is one of the essential and challenging problems in computer vision and pattern recognition …

The aspiration of the next generation's autonomous driving (AD) technology relies on the
dedicated integration and interaction among intelligent perception, prediction, planning, and …

Due to the difficulty in generating the effective descriptors which are robust to occlusion and
viewpoint changes, place recognition for 3D point cloud remains an open issue. Unlike most …

Compared with global average pooling in existing deep convolutional neural networks
(CNNs), global covariance pooling can capture richer statistics of deep features, having …

Exploiting the spatial structure in scene images is a key research direction for scene
recognition. Due to the large intra-class structural diversity, building and modeling flexible …

Scene classification, aiming at classifying a scene image to one of the predefined scene
categories by comprehending the entire image, is a longstanding, fundamental and …

Before the big data era, scene recognition was often approached with two-step inference
using localized intermediate representations (objects, topics, and so on). One of such …

Scene recognition is challenging due to the intra-class diversity and inter-class similarity.
Previous works recognize scenes either with global representations or with the intermediate …

The problem of transferring a deep convolutional network trained for object recognition to
the task of scene image classification is considered. An embedded implementation of the …

Scene classification based on convolutional neural networks (CNNs) has achieved great
success in recent years. In CNNs, the convolution operation performs well in extracting local …