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Titlebook: Big Visual Data Analysis; Scene Classification Chen Chen,Yuzhuo Ren,C.-C. Jay Kuo Book 2016 The Author(s) 2016 Big Visual Data Analysis.Sce

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发表于 2025-3-21 18:11:54 | 显示全部楼层 |阅读模式
期刊全称Big Visual Data Analysis
期刊简称Scene Classification
影响因子2023Chen Chen,Yuzhuo Ren,C.-C. Jay Kuo
视频video
发行地址Presents a comprehensive big visual data analysis methodology.that helps readers understand the topic quickly and fully.Includes abundant insightful data analysis results and comparisons that can be.u
学科分类SpringerBriefs in Electrical and Computer Engineering
图书封面Titlebook: Big Visual Data Analysis; Scene Classification Chen Chen,Yuzhuo Ren,C.-C. Jay Kuo Book 2016 The Author(s) 2016 Big Visual Data Analysis.Sce
影响因子This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoorscene classification, and outdoor scene layout estimation. It is illustrated with numerous naturaland synthetic color images,and extensive statistical analysis is provided to help readers visualize big visualdata distribution and the associatedproblems. Although therehas been some research on big visual data analysis, little workhas been published on big image data distribution analysis using the modernstatistical approach described in thisbook. By presenting a complete methodology on big visual data analysis withthree illustrative scene comprehensionproblems, it provides ageneric framework that canbe applied to other big visual data analysis tasks.
Pindex Book 2016
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书目名称Big Visual Data Analysis影响因子(影响力)




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书目名称Big Visual Data Analysis读者反馈学科排名




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发表于 2025-3-21 22:18:07 | 显示全部楼层
Introduction,ems become more challenging to traditional approaches. In this chapter, we will briefly review three important research problems, indoor/outdoor classification, outdoor scene categorization and geometric labeling. In addition, we will provide an overview of the book and its perspective benefits to t
发表于 2025-3-22 03:22:57 | 显示全部楼层
Indoor/Outdoor Classification with Multiple Experts,efficiently provide robust performance. In this chapter, we will firstly review the weakness of existing approaches and then propose a systematic machine-learning approach, Expert Decision Fusion (EDF) to obtain robust classification performance.
发表于 2025-3-22 06:05:32 | 显示全部楼层
Outdoor Scene Classification Using Labeled Segments, local rigid rectangle patches, which are sub-optimal to semantic segments. In this chapter, we will review the significance and problems of semantic segments in previous work and propose a robust semantic segmentation system as the state-of-the-art solution.
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Conclusion and Future Work,ditional approaches on large-scale visual data understanding problems. We will see great potential of proposed algorithms in the end of the brief. We will also point out possible directions of researches in the near future.
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