Aggregating Sparse Binarized Local Features by Summing for Efficient 3D Model Retrieval

An effective and widespread approach for shape-based 3D model retrieval (3DMR) is to use a feature vector per 3D model obtained by aggregating, or pooling, a set of local features extracted from the 3D model. State-of-the-art feature aggregation algorithms, such as Fisher Vector (FV) coding [7] or S...

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Bibliographic Details

Published in:	2016 IEEE Second International Conference on Multimedia Big Data (BigMM) pp. 314 - 321
Main Authors:	Furuya, Takahiko, Ohbuchi, Ryutarou
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.04.2016
Subjects:	Big data binary feature Conferences feature aggregation feature encoding local feature Multimedia communication shape-based 3D model retrieval sparse coding
Online Access:	Get full text
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