Many graph-based semi-supervised learning methods for large datasets have been
proposed to cope with the rapidly increasing size of data, such as Anchor Graph …

A simple yet effective object descriptor for visual tracking is proposed in this paper. We first
decompose the bounding box of a target object into multiple patches, which are described …

Graph-based semi-supervised learning, which further utilizes graph structure behind
samples for boosting semi-supervised learning, gains convincing results in several machine …

Image retagging aims to improve the tag quality of social images by completing the missing
tags, rectifying the noise-corrupted tags, and assigning new high-quality tags. Recent …

Minimum-cost flow algorithms have recently achieved state-of-the-art results in multi-object
tracking. However, they rely on the whole image sequence as input. When deployed in real …

This paper presents a novel tracking algorithm which integrates two complementary
trackers. Firstly, an improved Bayesian tracker (B-tracker) with adaptive learning rate is …

As the captured information in our real word is very scare and labeling sample is time cost
and expensive, semisupervised learning (SSL) has an important application in computer …

Visual tracking is challenging due to image variations caused by various factors, such as
object deformation, scale change, illumination change, and occlusion. Given the superior …

Protecting the register value and its data buses is crucial to reliable computing in high-
performance microprocessors due to the increasing susceptibility of CMOS circuitry to soft …

In this paper, we propose a novel and robust tracking framework based on online
discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain …