Extraction method of local reflection symmetry axis in image based on multi-instance subspace learning

A reflection symmetry and extraction method technology, applied in the field of computer vision, can solve the problems of large sample differences and difficulty in training classifiers, etc., and achieve the effect of high accuracy, easy implementation, and simple and easy feature extraction process

Active Publication Date: 2021-05-07
SHANGHAI UNIV
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Problems solved by technology

[0007] But on the other hand, for more complex problems like extracting symmetry axes, the samples are very different, and it is difficult to train a suitable classifier on the complete feature space.

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  • Extraction method of local reflection symmetry axis in image based on multi-instance subspace learning
  • Extraction method of local reflection symmetry axis in image based on multi-instance subspace learning
  • Extraction method of local reflection symmetry axis in image based on multi-instance subspace learning

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[0060] Such as figure 1 As shown in FIG. 4 , the symmetry axis extraction technology provided by the present invention proposes a new multi-instance learning method, that is, a multi-instance subspace learning method. This method uses partial random projection trees to divide multi-instance features into appropriate subspaces, and then trains classifiers for each subspace to achieve easier and more accurate symmetry axis extraction. It is a new symmetry axis extraction method. . First, for each sample point on the natural image in the training data set, the contour intensity features and self-similarity features are extracted at multiple scales and angles, and merged into a multi-instance feature bag of these sample points, and constructed using the feature bag of positive samples. Partially randomly project the tree, and then assign the negative samples to the appropriate leaf nodes (subspaces) according to the constructed projection rules, train their own classifiers for ea...

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Abstract

The present invention provides a method for extracting local reflection symmetry axes in images based on multi-instance subspace learning. The method includes the following steps: step 1, obtaining local image multi-instance feature packages of natural images; step 2, constructing partial random projection tree; Step 3, decision analysis: analyze the multi-instance feature package of the natural image whose symmetry axis is to be extracted and the multi-instance feature package in the training data set with a subspace classifier to obtain the analysis result. The feature extraction process of the invention is simple and easy, and the recall rate and accuracy rate of the extraction result are high.

Description

technical field [0001] The invention belongs to computer vision technology, and relates to a method for extracting local reflection symmetry axes in images based on multi-instance subspace learning. Background technique [0002] Symmetry axis, also known as central axis or skeleton, is an important underlying feature of local images, which can be used to describe the shape of objects in the image. In recent years, it has been widely used in many popular fields such as shape-based object recognition, biomedical image analysis, human body gesture recognition, and motion capture. The symmetry axis is divided into reflection symmetry axis, rotation symmetry axis, translation symmetry axis and so on. The most commonly used one is the reflection symmetry axis, which is the focus of research and application. Hereinafter, the reflection symmetry axis will be referred to as the symmetry axis for short. [0003] At present, most of the symmetry axis extraction methods rely on the eff...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/44G06F18/24
Inventor 沈为江远白翔张之江
Owner SHANGHAI UNIV
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