An Image Set Classification Method Based on Aggregate Hash Learning

Abstract

The invention discloses an image set classification method based on aggregated hash learning, comprising the following steps: S1, establishing a hash aggregation model according to a training image set; S2, training the hash aggregation model to obtain a linear hash after the training is completed. Hash function and the instance hash code matrix of the training image set; S3, calculate the hash code of the instance of the image set to be classified according to the linear hash function completed by the training; S4, according to the hash code of the instance of the image set to be classified and The distance between the instance hash code matrices of the training image set is used to obtain the category of the image set to be classified; the present invention solves the problem that the existing hash learning method does not take into account the set information of the image set well, and is difficult to perform well in the Hamming space Keeping the similarity of instances, it is difficult to obtain high-quality instance hash codes, which in turn affects the classification accuracy of image sets.

Description

technical field [0001] The invention relates to the technical field of image classification, in particular to an image set classification method based on aggregated hash learning. Background technique [0002] Over the years, with the rapid development of multimedia technology, image set classification has been widely used in a large number of real-world applications, such as object classification, video-based face recognition. Compared with traditional single-shot image-based classification tasks, image set classification pays more attention to the generality of the set and the complementarity of samples, which can provide richer information to describe the corresponding topics. The interest in image set classification has been steadily growing in the research community as image set classification can provide more promising performance to overcome the appearance variations of images (e.g. appearance, occlusion, lighting, expression, pose). However, with the increase of dat...

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Problems solved by technology

[0004] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides an image set classification method based on aggregated hash learning to solve the problem that the existing hash learning method does not consider the set information of the image set well, and it is difficult to Hamming space maintains the similarity of instances, and it is difficult to obtain high-quality instance hash codes, which in turn affects the classification accuracy of image sets

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