.png){kind=icon}
返回
An efficient concept detection system via sparse ensemble learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2014.09.100.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we present an efficient concept detection system based on a novel bag of words extraction method and sparse ensemble learning. The presented system can efficiently build the concept detectors upon large scale image dataset, and achieve real-time concept detection on unseen images with the state-of-the-arts accuracy. To do so, we first develop an efficient bag of visual words (BOW) construction method based on sparse non-negative matrix factorization (NMF) and GPU enabled SIFT feature extraction. We then develop a sparse ensemble learning method to build the detection model, which drastically reduces learning time in order of magnitude over traditional methods like Support Vector Machine. To overcome the difficulty of manual annotation of training dataset, we construct a large training set with both pseudo relevance feedback of negative samples and interactive feedback of positive samples. Experiments on TRECVID 2012 dataset and MIRFlickr-1M dataset show both efficiency and effectiveness of our system. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Ensemble learning
Visual concept detection
Bag of visual words
Sparse coding
Pseudo relevance feedback
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
.png){kind=icon}
Cancer care in a Western Indian tertiary center during the pandemic: Surgeon's perspective大流行期间西部印度三级中心癌症护理：外科医生视角
OAAI
OAAI分享收藏Dynamic Changes in Myofibroblasts Affect the Carcinogenesis and Prognosis of Bladder Cancer Associated With Tumor Microenvironment Remodeling肌成纤维细胞的动态变化与肿瘤微环境重塑对膀胱癌发生及预后的影响
OAAI
OAAI分享收藏分享收藏分享收藏
查看更多内容