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Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
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深度软件三重哈希用于多标签交叉模式检索.
Shuo Han1, Qibing Qin2, Jinkui Hou2
1School of Computer Science, Qufu Normal University, Rizhao, China.
概括
深软三重哈希 (DSTH) 通过使用每个类中的多个中心来捕获本地结构来改善交叉模式的检索. 这种新的深度哈希框架增强了嵌入式学习,并减少了语义差距,以获得更好的检索准确性.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 信息检索 信息检索
背景情况:
- 深度交叉模式散列方法通常使用小批次训练,限制邻近探索和嵌入质量,特别是在多标签数据上.
- 现有的SoftMax损失优化模型类具有单个中心,未能捕获类内语义集群并增加语义差距.
研究的目的:
- 提出Deep SoftTriple Hashing (DSTH),这是一个学习紧哈希代码的新框架,可以在跨模式检索中保持语义相似性.
- 通过建模类内变异和减少语义差距来解决当前深度哈希方法的局限性.
主要方法:
- 引入了每个类的多中心策略,以建模异质样本分布,并减少类内差异.
- 开发了一个类中心策略,使用L2,1规范化来汇总类似的中心,并确定最佳的中心数量.
- 整合了一个语义位置量化损失,以最大限度地减少量化错误,并提高二进制代码的可区分性.
主要成果:
- DSTH在跨模式检索方面取得了显著的改进,在三个多标签数据集上,绝对的mAP收益比强有力的基线高1.2%-9.3%.
- 与现有方法相比,在精密召回曲线上表现优越.
- 有效地学习了紧的哈希代码,更好地保留语义相似性.
结论:
- 在复杂的多标签检索场景中,DSTH有效地使用多个中心模拟类内局部结构,优于单中心方法.
- 拟议的框架增强了嵌入式学习,并减少了语义差距,从而在跨模式检索中实现了最先进的性能.
- DSTH提供了一种有前途的方法来改进大规模的跨模式检索系统.
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