TAILIEUCHUNG - Product sub-vector quantization for feature indexing

This work addresses the problem of feature indexing to significantly accelerate the matching process which is commonly known as a cumbersome task in many computer vision applications. To this aim, we propose to perform product sub-vector quantization (PSVQ) to create finer representation of underlying data while still maintaining reasonable memory allocation. | Journal of Computer Science and Cybernetics 2019 69-83 DOI 1813-9663 35 1 13442 PRODUCT SUB-VECTOR QUANTIZATION FOR FEATURE INDEXING THE-ANH PHAM1 DINH-NGHIEP LE1 THI-LAN-PHUONG NGUYEN2 1Hong Duc University 2 Thai Nguyen University Lao Cai Campus phamtheanh@ Crossref Similarity Check Abstract. This work addresses the problem of feature indexing to significantly accelerate the matching process which is commonly known as a cumbersome task in many computer vision applications. To this aim we propose to perform product sub-vector quantization PSVQ to create finer representation of underlying data while still maintaining reasonable memory allocation. In addition the quantized data can be jointly used with a clustering tree to perform approximate nearest search very efficiently. Experimental results demonstrate the superiority of the proposed method for different datasets in comparison with other methods. Keywords. Product quantization Hierarchical clustering tree Approximate nearest search. 1. INTRODUCTION Feature indexing has been known as an important technique which allows fast retrieval and matching of visual objects in computer vision filed. The most active application of feature indexing is probably concerned with fast approximate nearest neighbor ANN search. In the literature popular approaches for this problem can be listed as space partitioning methods typically KD-tree 5 and randomized KD-trees 26 or LM-tree 24 hashing methods such as LSH 8 Kernelized LSH 12 hierarchical clustering methods such as vocabulary K-means tree 19 POC-trees 22 . Recently product quantization PQ 9 has been actively studied for its applications in fast approximate nearest neighbor search ANN and feature indexing. Different variants of PQ technique have been presented to optimize the quantization stage such as optimized PQ 6 20 locally optimized PQ 10 or distribution sensitive PQ DSPQ 13 . PQ can be also combined with hierarchical clustering idea to boost the

• Quản trị Web
• Product quantization
• Hierarchical clustering tree
• Approximate nearest search
• Product sub vector quantization
• Feature indexing
• Approximate nearest neighbor
• Terms—Feature indexing
• Approximate nearest neighbor search
• Hierarchical clustering decomposition
• Optimizing Typed Feature
• Structure Grammar Parsing
• Non Statistical Indexing
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Protein structure
• Feature selection
• A protein structure consists
• Previous feature selection methods
• Protein classification dataset
• BMC Bioinformatics
• Text categorization
• Feature engineering
• Biomedical literature
• MeSH indexing