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Image retrieval method based on self-adaptive rectangular window

A rectangular window and image retrieval technology, applied in the field of information processing, can solve the problems of reducing the similarity between the target area to be retrieved and the image in the database, and reducing the retrieval accuracy

Inactive Publication Date: 2017-06-20
LIAOCHENG UNIV
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AI Technical Summary

Problems solved by technology

This method introduces the concept of layering on the basis of K-Means clustering. Although it reduces the time of traditional K-Means clustering, it still represents each database image as a single image vector, which also reduces the number of target areas to be retrieved. The similarity between images and database images, thereby reducing the retrieval accuracy

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  • Image retrieval method based on self-adaptive rectangular window
  • Image retrieval method based on self-adaptive rectangular window
  • Image retrieval method based on self-adaptive rectangular window

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Experimental program
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Embodiment 1

[0058] like Figure 1 to Figure 3 Shown: An image retrieval method based on an adaptive rectangular window, the specific implementation steps are as follows

[0059] Extract locally invariant features for database images.

[0060] In the database image I i , the extracted SURF descriptor is expressed as in: is the image I i In the rth descriptor, the dimension is 128 dimensions, n i is the image I i The number of SIFT descriptors in .

[0061] The spatial coordinates of feature descriptors in each database image are clustered using the G-means method.

[0062] In the G-means clustering process, assuming that the feature points in the image satisfy the Gaussian distribution, in the database image I i Randomly initially select the two-dimensional space coordinates of k=2 SURF descriptors as the cluster centers C=(c 1 ,c 2 ), create a random kd tree according to the clustering center C, and use the random kd tree to perform an approximate nearest neighbor search on th...

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Abstract

The invention discloses an image retrieval method based on a self-adaptive rectangular window. The method specifically comprises the following steps: step A, extracting local unchanged characteristics of database images, clustering space coordinates of characteristic descriptors in each database image using a G-means method; step B, according to classification of G-means clustering in each database image, establishing a self-adaptive rectangular window, and removing a sparse rectangular window, merging rectangular windows, and removing small rectangular windows; step C, respectively vectorizing the self-adaptive rectangular windows in the database images, establishing self-adaptive window vectors, and based on the window vectors, establishing an inverted index; step D, vectorizing a to-be-retrieved target region, performing similarity search in the inverted index, to giving a final retrieval result. Compared with the prior art, the method improves retrieval accuracy while ensuring retrieval efficiency.

Description

technical field [0001] The invention belongs to the technical field of information processing, and in particular relates to an image retrieval method based on an adaptive rectangular window. Background technique [0002] With the rapid development of Internet technology and the large-scale popularization of digital products, the acquisition of images has become more convenient and convenient, and the number of images has shown explosive growth, which poses a huge challenge and test to the storage, search and organization of images. In real life, how to quickly and accurately obtain the most useful information in a large-scale image database has become one of the focuses of people's attention and research. [0003] Image retrieval technology refers to searching in the image database and finding relevant images that meet the conditions according to the query image content information or specified query criteria. Most of the traditional image retrieval technologies use text-ba...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 冯德瀛赵颖刘从新孙哲
Owner LIAOCHENG UNIV
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