An Image Feature Encoding and Recognition Method Based on Ensemble Learning
An image feature and integrated learning technology, applied in image coding, image data processing, instruments, etc., can solve the problems that affect the performance of image content recognition, and it is difficult to accurately reflect the similarity of image perception content, so as to achieve accurate image recognition and enhanced accuracy High rate, high robustness and discriminative effect
Active Publication Date: 2017-09-08
TIANJIN UNIV
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Problems solved by technology
However, existing studies have shown that the distance between the quantitative indexes of features is difficult to accurately reflect the similarity of image perception content, which affects the performance of image content recognition.
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[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the implementation manners of the present invention will be further described in detail below.
[0024] The embodiment of the present invention proposes an image feature encoding and recognition method based on ensemble learning, see figure 1 , the implementation process of the present invention is illustrated here by taking the adaptive integrated learning algorithm [6] as an example:
[0025] 101: Construct training samples and initialize sample sampling probability;
[0026] 1) select N pairs of training images, extract the feature vectors of each training image to form training samples, and the present invention does not limit the feature extraction method;
[0027] Among them, N / 2 pairs are composed of image pairs with the same source of content, that is, one of them is the version of the other after the content is preserved and distorted (such as filtering, adding no...
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The invention discloses an image feature encoding and recognition method based on integrated learning, which relates to the technical field of signal and information processing, including: constructing training samples, initializing sample sampling probability; selecting training samples according to the sampling probability, and using the sampled samples to train features The encoding function classifies the training samples; adaptively updates the sampling probability of the training samples according to the classification results; loops the above sampling and training process until the training stop condition is met. The feature encoding function trained by this method can map any image feature vector into a short hash sequence, and at the same time, it can make the distance between hashes match the perceptual similarity between images to the greatest extent, and has low computational complexity. features. The test results show that the image hash generated by the present invention can show higher recognition accuracy in image content recognition.
Description
technical field [0001] The invention relates to the technical field of signal and information processing, in particular to an image feature encoding and recognition method based on integrated learning. Background technique [0002] Content recognition is the core technology to solve image query, indexing and copyright management problems. The goal of image content recognition is to search for homologous images with the same perceptual content as the image to be queried in a large database or network. The premise of image content recognition is to describe the perceptual content of the image. To reduce the complexity of image content recognition, it is usually necessary to encode image features into short descriptors. By comparing the descriptors, the perceptual similarity of the images can be judged. In order to achieve accurate image content recognition, the original image and its homologous version (such as the version of the original image after lossy compression, filte...
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IPC IPC(8): G06T9/00
Inventor 李岳楠王萍苏育挺
Owner TIANJIN UNIV