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Unsupervised depth hashing method based on target detection

A target detection, unsupervised technology, applied in the direction of digital data information retrieval, special data processing applications, instruments, etc., can solve the problem of not being able to use pictures

Inactive Publication Date: 2019-09-03
BEIJING INSTITUTE OF TECHNOLOGYGY +1
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AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the latent label information in the picture cannot be used in the deep unsupervised hashing method, and proposes an unsupervised deep hashing method based on target detection, which can use the potential semantic labels in the picture to improve the picture retrieved MAP value

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  • Unsupervised depth hashing method based on target detection
  • Unsupervised depth hashing method based on target detection

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Embodiment 1

[0075] This embodiment describes the process of processing the public image data set VOC 2007 data set of the present invention. The frame diagram of the unsupervised deep hashing model involved in this embodiment is as follows figure 1 shown.

[0076] Among them, the public image dataset VOC 2007 dataset is a real image set, which collects 9963 images, including a total of 20 categories, namely: airplanes, bicycles, birds, boats, bottles, buses, cars, Cat, chair, cow, dining table, dog, horse, motorcycle, person, potted plant, sheep, sofa, train, TV monitor.

[0077] The specific process of this embodiment is as follows figure 2 shown, the specific steps are as follows:

[0078] Step A: Select the YOLO target detection method, and train the method on the large-scale ImageNet image data set according to the training steps of the selected target detection method. Output the trained target detection unit;

[0079] The ImageNet image dataset is a large-scale visualization da...

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Abstract

The invention relates to an unsupervised depth hashing method based on target detection, and belongs to the technical field of computer information retrieval and picture retrieval. The method comprises the steps of obtaining the object tags existing in the pictures by utilizing the target detection, taking the tags as pseudo tags of the pictures, and training a designed end-to-end depth hash modelbased on the pseudo tags to obtain the hash code representation of each picture in a Hamming space; evaluating the quality of the deep Hash model through the average accuracy mean value of the corresponding Hash codes in the picture retrieval task, wherein the average accuracy rate mean value is the MAP, and the unsupervised deep hash model comprises a target detection algorithm unit and a hash network unit. According to the method, the more instructive information can be obtained, the capability of a depth model can be fully utilized to learn the high-quality Hash codes with maintained similarity, and the picture retrieval is carried out in a real picture data set to obtain the best effect, namely, the MAP value is the highest.

Description

technical field [0001] The invention relates to an unsupervised deep hashing method based on target detection, and belongs to the technical fields of computer information retrieval technology and picture retrieval technology. Background technique [0002] With the rapid growth of image data, approximate nearest neighbor (ANN) search has received more and more attention from researchers in the field of large-scale image search. In the existing artificial neural network search technology, the hash method that preserves the similarity has the advantages of high retrieval efficiency and low storage cost. The main idea of ​​hashing methods is to convert high-dimensional data points into a set of compact binary codes while maintaining the similarity of the original data points. Since the raw data points are represented in binary codes instead of real-valued features, the time and memory overhead of searching can be greatly reduced. [0003] At present, most of the hashing method...

Claims

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Application Information

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IPC IPC(8): G06F16/583
CPCG06F16/583
Inventor 毛先领涂荣成黄河燕程序邹佳
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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