Efficient 3D Object Retrieval Based on Compact Views and Hamming Embedding
| Autor: | L. Sun, Qiang Cai, Haisheng Li, Xiaobin Zhu, Shuilong Dong, Junping Du |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Similarity (geometry)
DSP-SIFT features General Computer Science Matching (graph theory) Computer science Feature extraction Scale-invariant feature transform 02 engineering and technology Background noise hamming embedding 0202 electrical engineering electronic engineering information engineering General Materials Science Electrical and Electronic Engineering view-based 3D object retrieval business.industry Quantization (signal processing) General Engineering 020207 software engineering Pattern recognition Object (computer science) Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business Hamming code lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 6, Pp 31854-31861 (2018) |
| ISSN: | 2169-3536 |
| Popis: | View-based 3-D object retrieval techniques have been increasingly important in various applications of computer vision. In this paper, we present a novel framework for view-based 3-D object retrieval. First, we exclude the background of views to avoid the disturbance of background noise. Then for these views, we extract the domain-size pooled SIFT descriptor features and encode them using approximate K-means algorithm. After quantizing each object with the approximate near neighbor, the hamming embedding is applied to refine the descriptors by adding binary signatures. Finally, we use the hamming matching to measure the similarity between two 3-D objects. A large number of experiments are performed on the ETH-80 benchmark. Compared with the state-of-art methods, the proposed method is demonstrated to be effective and robust. |
| Databáze: | OpenAIRE |
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