An image retrieval method based on depth learning and approximate target location

A technology of target positioning and image retrieval, which is applied in the field of image retrieval of deep learning and approximate target positioning, and can solve problems such as unsatisfactory results.

Active Publication Date: 2018-12-18
XIAN UNIV OF TECH
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Problems solved by technology

Although CroW can solve most image retrieval problems, the effect can only be unsatisfactory when distinguishing complex backgrounds

Method used

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  • An image retrieval method based on depth learning and approximate target location
  • An image retrieval method based on depth learning and approximate target location
  • An image retrieval method based on depth learning and approximate target location

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Embodiment Construction

[0064] The present invention will be further elaborated below in conjunction with the accompanying drawings and specific embodiments.

[0065] Frame diagram of image retrieval algorithm for deep learning and approximate target localization, such as figure 1 As shown, from the input and output of the algorithm, the present invention inputs two image libraries (query image library, image library to be processed), wherein the two images are weighted through the F-CroW process in the feature extraction and weighting stages to obtain N a similar target area.

[0066] From the perspective of algorithm flow, the present invention generally includes three steps: feature extraction and weighting, approximate target location and image reordering. In the feature extraction and weighting stage, the activation map generated by the last layer of the 'pooling' layer in the convolutional neural network convolution layer is extracted, and the extracted features are subjected to spatial weight...

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Abstract

The invention discloses an image retrieval method based on depth learning and approximate target location. The method comprises the steps of focusing on searching the approximate object region in theimage; extracting the corresponding local CNN features by using multi-scale sliding windows on the image. With these small feature blocks, the feature vectors of the image can be obtained, which can meet the requirements of image retrieval. After processing the features, optimized image representations with several weights have been prepared for image retrieval and rearrangement; a new frame called approximate object position is constructed to search the most similar domain which can be retrieved from the queried image. For the retrieval of a specific object, a series of small feature blocks are used to locate the approximate object on the database image, and the retrieval process is completed by the region located. The invention fully utilizes the powerful ability of depth learning, optimizes and innovates based on CNN, improves image retrieval accuracy and reduces redundancy.

Description

technical field [0001] The invention belongs to the technical field of image analysis and retrieval methods, and in particular relates to an image retrieval method for deep learning and approximate target positioning. Background technique [0002] In recent years, with the continuous promotion and development of the machine intelligence industry and the Internet + industry, image retrieval has attracted more and more attention from researchers, and has gradually penetrated into the bottom layer of neural networks, computer networks, machine learning and other fields, and has become an important tool for the society. The backbone of services, industrial upgrading, etc. [0003] At present, image retrieval in most industrial-grade applications still stays in the time-consuming and labor-intensive method based on text or keywords. Of course, content-based image retrieval (Content-based Image Retrieval) has been widely deployed in more advanced search engines, but often a singl...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06N3/04
CPCG06N3/045
Inventor 廖开阳邓轩郑元林汤梓伟袁晖
Owner XIAN UNIV OF TECH
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