A large-scale image vocabulary tree retrieval method and system based on GPU acceleration

Abstract

The invention discloses a large-scale image vocabulary tree retrieval method, which belongs to the field of computer vision. According to the structural characteristics of the GPU platform, the algorithm of the present invention adopts a novel vocabulary tree data structure, and modularizes the traditional SIFT feature points, so that it can make full use of the large-scale parallel computing capability of the GPU, greatly improving the large-scale Efficiency of Image SIFT Feature Point Mapping. After mapping the batch of SIFT feature points into a histogram, the original inverted index scheme is enhanced and improved by using a fast compaction method, so that the unique expression of visual vocabulary, histogram normalization and query image Scoring has been accelerated to a certain extent. The invention also realizes a GPU-accelerated large-scale image vocabulary tree retrieval system, which not only has great acceleration significance for image retrieval itself, but also has a huge impact on the efficiency of three-dimensional reconstruction algorithms for large-scale scenes.

Description

technical field [0001] The invention belongs to the field of computer vision, and more specifically relates to a large-scale image vocabulary tree retrieval method and system based on GPU acceleration. Background technique [0002] The vocabulary tree algorithm proposed by David Nistér et al uses a tree structure to quantify visual vocabulary, integrates the quantification and indexing of visual vocabulary, and combines text search technology to speed up the similarity judgment of image descriptors. It has high accuracy and efficiency, and is widely used in target recognition, scene recognition and 3D reconstruction. [0003] The original vocabulary tree algorithm mainly includes three stages: visual vocabulary learning, vocabulary tree construction and vocabulary tree query. [0004] In the visual vocabulary learning stage, hierarchical k-means clustering is employed to generate structured visual vocabulary. First, it is necessary to extract the Scale-Invariant Feature Tr...

AI Technical Summary

Problems solved by technology

[0007] Since the SIFT feature points of all database images need to be mapped to the corresponding visual vocabulary in the construction stage of the vocabulary tree, although the original vocabulary tree algorithm uses a hierarchical tree structure for comparison and query, the efficiency has been improved to a certain extent, but In the face of the current widespread large-scale image retrieval, this stage will still occupy a huge computational overhead of the entire algorithm

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