Dimensionality reduction for histogram features: A distance-adaptive approach

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• 作者：Miaomiao CuiAuthor Vitae ; Jiangtao CuiAuthor Vitae ; Hui Li ; ^{hli@xidian.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Histogram features ; Dimensionality reduction ; MDS ; Similarity measure
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：173
• 期：part_P2
• 页码：181-195
• 全文大小：2229 K

文摘

Histogram representations of visual features, such as high dimensional Bag-of-Features (BOF) and Spatial Pyramid Matching (SPM) representation, have been widely studied and adopted in image classification and retrieval due to their simplicity and performance. Problems involving high dimensional feature vectors usually require much computational cost and huge storage space. Moreover, it may additionally suffer from low accuracy because of the noise in data. In this paper we propose a novel distance-adaptive dimensionality reduction framework, namely generalized Multidimensional Scaling, with linear coding time to create compact and discriminative BOF or SPM representations. Comparing with traditional MDS, our approach exhibits two advantages, on one hand it is adaptive to many measures; on the other hand, it is able to map arbitrary query points into the new space. Exhaustive experimental results show that a very low dimension of BOF or SPM is sufficient for the retrieval task without losing accuracy. Comparatively, the state-of-the-art methods cannot achieve high accuracy on the low dimension. Aside from image retrieval task, we also show that our approach is much more effective than the original histogram representations when applied in image classification task.