Accurate Aggregation of Local Features by using K-sparse Autoencoder for 3D Model Retrieval

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.