Image classifying method for combining vision vocabulary books of different sizes

Abstract

The invention discloses an image classifying method for combining vision vocabulary books of different sizes and relates to the technical field of model recognition, computer vision and image understanding. According to the image classifying method disclosed by the invention, multi-resolution information is used for quantizing images and a plurality of available clues from different comprehensive layers are used for classifying the images in parallel. In order to utilize information of different particles to classify the images, the images are quantized based on the vision vocabulary books of different sizes; and the vision vocabulary books of different sizes can be used for capturing different image characteristics. Then, the images are trained based on the vision vocabulary books of different sizes to obtain different quantization vector sets so as to learn different classifiers; and each classifier can obtain different models of objects according to the information of different granularities of the images to integrate classifier models to classify new images to obtain a better effect. The experimental result shows that the performance of the vision vocabulary books of single size can be obviously improved and the robustness is very strong, so that the image classifying method can be used for achieving a good classifying effect on the different images.

Description

technical field [0001] The invention belongs to the technical fields of pattern recognition, computer vision and image understanding, and in particular relates to an image classification method. Background technique [0002] The difficulty of image classification lies in the need to establish a class model that can accommodate the height variation within a class and distinguish between different classes. The "Constellation" model attempts to locate different object parts and determine their spatial relationship. Although these methods may be expressive, such spatially constrained models cannot handle or recognize large deformations, such as rotations and occlusions that do not lie in a plane, nor do they consider objects with an uncertain number of localities, such as buildings and trees. Many popular methods for image classification represent images using collections of independent blocks described by local visual descriptors, most typical of which are "bag-of-words" model...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that image classification cannot effectively integrate multiple information and due to the high dimensionality of vectors describing images, traditional machine learning methods tend to produce models that are very unstable and have poor generalization ability, the present invention provides a method that combines different sizes Image classification method for visual vocabulary