Designing effective features is a fundamental problem in computer vision. However, it is
usually difficult to achieve a great tradeoff between discriminative power and robustness …

In this paper, we propose to learn a discriminative and shareable feature transformation filter
bank to transform local image patches (represented as raw pixel values) into features for …

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 …

This article presents a new supervised multilayer subnetwork-based feature refinement and
classification model for representation learning. The novelties of this algorithm are as …

In order to encode the class correlation and class specific information in image
representation, we propose a new local feature learning approach named Deep …

The background information is a significant prior for salient object detection, especially when
images contain cluttered background and diverse object parts. In this paper, we propose a …

Multi-task learning has received great interest recently in the area of machine learning. It
shows a considerable capacity to jointly learn multiple latent relationships hidden among …

As one of the primary means of Earth observation, high-spatial-resolution remote sensing
images can describe the geometry, texture and structure of objects in detail. It has become a …

To achieve high scene classification performance of high spatial resolution remote sensing
images (HSR-RSIs), it is important to learn a discriminative space in which the distance …

In the semantic multinomial framework patches and images are modeled as points in a
semantic probability simplex. Patch theme models are learned resorting to weak supervision …