Accurate Aggregation of Local Features by using K-sparse Autoencoder for 3D Model Retrieval
Accurate Aggregation of Local Features by using K-sparse Autoencoder for 3D Model Retrieval
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DOI：
10.1145/2911996.2912054
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
T. Furuya;Ryutarou Ohbuchi
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文献类型：
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作者：
T. Furuya;Ryutarou Ohbuchi
聚集一组局部特征已被广泛用于实现包括二维图像和三维模型的多媒体数据的识别或检索。许多特征聚合算法（例如，特征袋（Bag-of-Features）、局部约束线性编码或Fisher向量编码）。它们首先通过对局部特征进行聚类来学习码本或一组码字，然后通过使用学习的码本来编码这些局部特征。尽管这些特征聚合算法取得了巨大的成功，但我们认为它们在准确性方面并不一定是最佳的，因为它们的码本学习和特征编码是分开计算的。在本文中，我们提出了两个新的特征聚合算法的基础上，k-Sparse自动编码器（kSA），实现更准确的局部特征聚合。我们提出的算法，称为数据库自适应kSA（DkSA）聚合和每数据自适应kSA（PkSA）聚合，共同优化码本学习和特征编码。此外，基于kSA的特征编码由于k-稀疏性约束和非负性约束而增强了局部特征的显著性。在所提出的两种算法中，PkSA聚合利用从kSA获得的局部特征的重构误差以获得更准确的聚合特征。基于形状的3D模型检索场景的实验评价表明，我们提出的算法的检索精度上级现有的特征聚合算法，我们相比。
Aggregating a set of local features has been used widely to realize recognition or retrieval of multimedia data including 2D images and 3D models. A number of feature aggregation algorithms (e.g., Bag-of-Features, Locality-constrained Linear coding, or Fisher Vector coding) have been proposed. They first learn a codebook, or a set of codewords, by clustering the local features and then encode these local features by using the learned codebook. Despite the great success of these feature aggregation algorithms, we argue that they are not necessarily optimal in terms of accuracy since their codebook learning and feature encoding are computed separately. In this paper, we propose two novel feature aggregation algorithms based on k-Sparse Autoencoder (kSA) that realize more accurate local feature aggregation. Our proposed algorithms, called Database-adaptive kSA (DkSA) aggregation and Per-data-adaptive kSA (PkSA) aggregation, jointly optimize codebook learning and feature encoding. In addition, the kSA-based feature encoding enhances saliency of local features due to k-sparseness constraints and non-negativity constraints. Of the two proposed algorithms, the PkSA aggregation exploits reconstruction error of a local feature derived from the kSA for more accurate aggregated feature. Experimental evaluation using a shape-based 3D model retrieval scenario showed that the retrieval accuracy of our proposed algorithms are superior to the existing feature aggregation algorithms we have compared against.