A Similar Image Retrieval Method Based on Local Feature Neighborhood Information

Abstract

The present invention relates to a similar picture retrieval method based on local feature neighborhood information, comprising: 1) acquiring training pictures; 2) using Hessian-Affine feature point detection algorithm and SIFT local feature descriptor to feature the pictures in multi-scale space Detect and describe; 3) according to step 2) feature structure corresponding shadow feature extracted; 4) utilize k-means clustering algorithm to step 2) the feature extracted in step 2) is clustered and generates the visual dictionary that comprises K visual words; 5) Map all the above-mentioned features one by one to the visual vocabulary with the smallest L2 distance, and store in the inverted index structure; 6) Save the inverted index to form a query database; 7) Obtain the corresponding inverted index of the query picture , and compare it with the query database to obtain a list of search results. Compared with the prior art, the invention has the advantages of high image retrieval accuracy and the like.

Description

technical field [0001] The invention relates to a picture retrieval method, in particular to a similar picture retrieval method based on local feature neighborhood information. Background technique [0002] In recent years, computer vision has been developing rapidly. Among them, similar image retrieval is a basic but challenging task, so it has attracted much attention. [0003] Currently, the bag-of-words model based on local features and inverted index structure is one of the most commonly used image retrieval models. Image local features are a type of features used in the field of image processing. Finding extreme points in the scale space, extracting position, scale, and rotation invariants can detect key points in the image. The bag-of-words model is an approximate method for feature matching. In this model, a local feature is quantized to its nearest visual word in a pre-trained dictionary and stored in an inverted index for querying. [0004] However, the above i...

AI Technical Summary

Problems solved by technology

[0005] First, due to the locality of features and quantization errors, the feature matching accuracy is very low, and there are a large number of wrong matches, which affect the final retrieval accuracy

[0006] Second, a large number of algorithms focus on the information of the feature points themselves, ignoring the strong correlation between the neighborhood information of the feature points and the feature points

[0007] Third, some algorithms try to use the spatial relationship between feature points for spatial verification, but such methods generally consume additional computing resources and steps, and the computational efficiency is not high

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