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An Open Set Recognition Method for Tree Trunk Image

A recognition method and image technology, applied in character and pattern recognition, instruments, biological neural network models, etc., can solve problems such as limiting the application range of K-means

Active Publication Date: 2021-04-06
ZHEJIANG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, the number K of clusters in the algorithm often needs to be given artificially, which limits the application range of K-means in actual scenarios

Method used

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  • An Open Set Recognition Method for Tree Trunk Image
  • An Open Set Recognition Method for Tree Trunk Image
  • An Open Set Recognition Method for Tree Trunk Image

Examples

Experimental program
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Effect test

experiment example

[0064] In order to verify the classification effect of the above-mentioned open-set recognition method for tree trunk images, the above-mentioned open-set recognition method was tested on the actual collected tree trunk data set.

[0065]The tree trunk data set is a data set collected by the team. The data set contains 13 kinds of tree trunks, and each tree trunk contains about 200 to 500 trunk RPG images. In the experiment, the first seven types of trunks were selected as the test objects for the experiment. In order to make the data set meet the requirement of training, the data of this kind of trunk image is expanded by randomly intercepting part of the trunk, and then grayscale processing is performed, and finally all the trunk images are processed into a size of 256×256. In the end, a data set of 500 pictures of each tree trunk and a total of 3,500 tree trunks was obtained. In the experiment, use such as image 3 The first 80% of the data of the first 4 types of trunks ...

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Abstract

The invention discloses an open-set recognition method for tree trunk images, comprising the following steps: (1) designing a CNN model, using part of training samples to train the CNN model, and constructing a feature extractor; (2) extracting feature maps, calculate the similarity between the feature maps, and use the DBSCAN algorithm to cluster the feature maps; (3) design the Loss function according to the similarity between the feature maps and the clustering results of the feature maps, and according to the Loss function to optimize the parameters of the feature extractor and the weight parameters of the similarity function; (4) input the trunk image to be classified into the optimized feature extractor, and then use the optimized similarity function to calculate the similarity between the output feature maps , and finally obtain the DBSCAN clustering result of the feature map according to the calculated similarity value. The open set recognition method can well realize the open set recognition of tree trunk images of unknown categories.

Description

technical field [0001] The invention belongs to the research field of deep learning algorithms and open set recognition in the field of artificial intelligence, and in particular relates to an open set recognition method for tree trunk images. Background technique [0002] For a long time, the open set recognition method has been a research hotspot in the field of deep learning. This method aims to discover unknown samples from data and extract useful feature information from them, and has wide applications in signal recognition and face detection. Jain et al. proposed an open set recognition method based on multi-classification SVM. They used SVM to learn probability decision scores and rejected unknown samples by setting probability thresholds. Bendale et al. proposed a method of using deep neural networks for open set recognition. They introduced the OpenMax layer and used this layer to estimate the probability that the input sample belongs to the unknown class. Ge et a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/46G06K9/62G06N3/04
CPCG06V10/40G06N3/045G06F18/23
Inventor 陈晋音林翔贾澄钰杨东勇
Owner ZHEJIANG UNIV OF TECH
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