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A Knowledge Base Construction Method Based on Image Recognition
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A construction method and image recognition technology, applied in the field of knowledge base construction based on image recognition, can solve the problems of unrealizable knowledge accumulation, low knowledge reusability, and low efficiency.
Active Publication Date: 2021-12-10
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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But at present, in order to obtain information and knowledge in images, people basically can only identify useful information through long-term viewing, and artificially integrate information to make judgments
This way of acquiring knowledge is not only inefficient, but also has a low degree of reusability of knowledge, and the accumulation of knowledge cannot be realized.
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[0033] figure 1 It is a flowchart of a method for building a knowledge base based on image recognition in the present invention.
[0034] In this example, if figure 1 Shown, a kind of knowledge base construction method based on image recognition of the present invention comprises the following steps:
[0035] S1. Obtain the target image
[0036] Obtain images containing multiple scenes and entities related to the knowledge base to be constructed, and each image contains multiple entities E 1 ,E 2 ,...,E i , ..., and each entity has multiple attribute values A 1,1 、A 1,2 ,...,A 2,1 、A 2,2 ,...,A i,j ,...;
[0037] In this embodiment, each image contains a scene such as the sky, and multiple entities such as people, objects, animals, etc., and each image needs to meet the basic definition, that is, the definition sufficient for human eyes to recognize.
[0038] S2. Target image preprocessing
[0039] First convert each image into a grayscale image, then smooth and ...
example
[0059] This embodiment builds a personnel information knowledge base based on monitoring images, which is used to assist the public security organs to quickly identify suspects, describe the suspect's movement trajectory, and help them quickly solve cases.
[0060] First, the image set collected from the case-related monitoring equipment is preprocessed according to the above method.
[0061] Second, the image is recognized using a neural network model. In this embodiment, the main task used for entity recognition is character recognition, and the attributes involved include: human body shape (fat and thin degree), clothing color, height size, which can be subdivided into: human body shape (thin, normal) , fat), clothing color (red, orange, yellow, green, blue, blue, purple), height size (less than 1.5 meters, 1.5-1.6 meters, 1.6-1.7 meters, 1.7-1.8 meters, 1.8-1.9 meters, 1.9 meters meters or more).
[0062] Finally, the coincidence degree of the pixel range of the entity a...
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Abstract
The invention discloses a method for constructing a knowledge base based on image recognition. Firstly, a series of images related to the target knowledge base are obtained and image preprocessing is performed, and then three neural network groups are used to identify the input image to obtain the scene of the image content, Entity and attribute information, and according to the coincidence degree of the pixel range occupied by the entity and the pixel range occupied by the attribute, and finally match the output entity and attribute according to the coincidence degree, and complete the construction of the knowledge base based on the triplet of scene, entity and attribute ; In this way, using images as the source of knowledge base construction not only expands the construction method of knowledge base, but also reduces the redundancy of knowledge base and improves query efficiency, and realizes the accumulation and reusability of image knowledge.
Description
technical field [0001] The invention belongs to the technical field of machine learning and information processing, and more specifically relates to a method for constructing a knowledge base based on image recognition. Background technique [0002] In the context of the information age, Internet technology has not only shortened the communication distances around the world step by step, enabling information to be disseminated and communicated quickly, but also accompanied by the rapid popularization of cheap electronic terminals resulting from the rapid iteration of industrial technology, each terminal Users not only become recipients of information, but also naturally become producers of information. One of the great changes that followed was the explosive export of knowledge. On the one hand, blowout knowledge output can be used as the data cornerstone of new technologies. For example, the deep learning technology that is most concerned by the society today is a represen...
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