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Near-repetitive image retrieval method based on consistent region deep learning features

A technology of deep learning and image retrieval, which is applied in the field of information security, can solve the problems of inapplicability to near-duplicate images and low retrieval efficiency, and achieve the effects of strong identification ability, improved accuracy, and reduced quantity

Active Publication Date: 2020-01-10
深圳市麦点传媒科技有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

[0009] Purpose of the invention: In order to solve the problems that the existing retrieval technology is not suitable for near-duplicate images sharing a small part of the duplicated area, and the retrieval efficiency is low; the present invention provides a near-duplicate image retrieval method based on deep learning features of consistent regions

Method used

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  • Near-repetitive image retrieval method based on consistent region deep learning features
  • Near-repetitive image retrieval method based on consistent region deep learning features
  • Near-repetitive image retrieval method based on consistent region deep learning features

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Embodiment Construction

[0035] The drawings constituting a part of the present invention are used to provide a further understanding of the present invention, and the schematic embodiments and descriptions of the present invention are used to explain the present invention, and do not constitute an improper limitation of the present invention.

[0036] like figure 1 As shown, this embodiment provides a near-duplicate image retrieval method based on consistent region deep learning features: in the offline stage, extract SIFT features for all images in the image library, and then use the K-Means clustering method to cluster each SIFT Features are quantized into visual words and stored into the constructed inverted index file. In the online stage, use the same feature extraction and quantization method for the input query image, calculate the similarity between the quantized SIFT features and the features in the index file, sort the obtained similarity results, and output the images related to the query ...

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Abstract

The invention discloses a near-repetitive image retrieval method based on consistent region deep learning features. The near-repetitive image retrieval method specifically comprises the following steps: extracting SIFT features of all images in an image library, quantizing the SIFT features into visual words, and establishing an inverted index file for all SIFT features; reserving K target regionsof each image, and calculating CNN features C(Rc) of the target region; extracting SIFT features of the query image, and quantizing the SIFT features into visual words; finding out candidate images by utilizing the inverted index file; finding out a nearly repeated region approximately repeated with each target region of each candidate image in the query image; extracting a CNN feature C(RQ) of the near-repetitive region; calculating cosine similarity between any C(Rc) and C(RQ) corresponding to the CNN feature, and taking the cosine similarity as a similarity score of the group; and in eachcandidate image, selecting a group of scores with the highest cosine similarity as similarity scores between the candidate image and the query image. According to the near-repetitive image retrieval method, the retrieval efficiency is improved, and meanwhile, the image retrieval accuracy is greatly improved.

Description

technical field [0001] The invention belongs to the field of information security, and in particular relates to a near-duplicate image retrieval method based on consistent region deep learning features. Background technique [0002] Due to the widespread use of powerful image processing tools and the rapid development of Internet technology, digital image data is increasingly easy to be illegally copied, tampered with and transmitted on the network. In fact, these illegal images are near-duplicate images that share small duplicated areas and undergo various image modifications such as rescaling, occlusion, noise addition, and brightness and color changes. To prevent image content from unauthorized use and privacy violation, detecting illegal partially reproduced versions of copyrighted images has become an urgent problem. Therefore, as a branch of content-based image retrieval, near-duplicate image retrieval plays a very important role in the fields of copyright and privacy...

Claims

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Application Information

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IPC IPC(8): G06F16/583G06K9/46G06K9/62G06N3/04
CPCG06F16/583G06V10/462G06N3/045G06F18/23213
Inventor 周志立孙文迪周煜孙星明
Owner 深圳市麦点传媒科技有限公司
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