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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Vydané v:2016 IEEE Second International Conference on Multimedia Big Data (BigMM) s. 314 - 321
Hlavní autori: Furuya, Takahiko, Ohbuchi, Ryutarou
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.04.2016
Predmet:
Big data
binary feature
Conferences
feature aggregation
feature encoding
local feature
Multimedia communication
shape-based 3D model retrieval
sparse coding
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Shrnutí: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 Super Vector (SV) coding [22], used in the approach is not spatially efficient, however. The FV or SV, for example, typically encodes a local feature into a very high-dimensional (e.g., 300k-dimensional) vector. For a database containing a large number of 3D models, the spatial cost of storing all the aggregated feature vectors for the database becomes very high. In this paper, we propose a novel, spatially efficient yet accurate feature aggregation algorithm called Sum of Sparse Binary codes (SSB) aggregation. The SSB first encodes a local feature into a highly sparse binary code. Then, a set of sparse binary codes are aggregated efficiently by simple summing into a compact feature vector. We also propose fast SSB (fSSB) aggregation, which is a computationally efficient approximation of the SSB. Experiments using a 3DMR scenario show that the proposed algorithms are significantly more efficient than the state-of-the-art feature aggregation algorithms we have compared against. At the same time, retrieval accuracies of the proposed algorithms are equal or better than the state-of-the-art aggregation algorithms.
DOI:10.1109/BigMM.2016.32