Automatic image annotation and translation method based on decision tree learning

Abstract

The invention discloses an automatic image annotation and translation method based on decision tree learning. A new image is automatically annotated, and a text word list with a visualized content is translated by a machine so as to realize the machine retrieval of image data, comprising a training annotation image set and image automatic annotations, wherein the training annotation image set utilizes an image segmentation algorithm to segment a training image set into sub areas and extract low-level visual features of each sub area; the feature data is discretized, and then the training annotation image set is classified by a clustering algorithm based on a low-level feature discrete value to construct a semantic dictionary; the low-level feature discrete value is used as an input attribute of the decision tree learning; and self training learning is carried out on the constructed dictionary by a decision tree machine learning corresponding to preset semantic concepts so as to generate a decision tree and obtain a corresponding decision rule. The training annotation image set has expandability and robustness and can improve the recall ratio and the precision ratio of the retrieval when the training annotation image set is applied to semantic image retrievals.

Description

technical field [0001] The invention relates to the fields of digital image retrieval technology and machine learning technology, in particular to a method for automatic image labeling and translation based on decision tree learning. Background technique [0002] In the early days, people realized image retrieval through manual annotation, but this work was time-consuming and laborious, especially when faced with large-scale network images, it was obviously not competent. Therefore, how to quickly and effectively realize the automatic semantic annotation of images has become very necessary. [0003] Automatic image annotation is a process of automatically assigning metadata to a digital image in the form of captions or keywords by a computer system. This computer vision application technique is used in image retrieval to organize and find images of interest to the user in the database. This method is called a multi-class image classification method, which contains a large ...

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Problems solved by technology

[0005] In view of the above deficiencies in the prior art, the purpose of the present invention is to study a method for automatic image labeling and translation based on decision tree learning, so that the training set after labeling has scalability and robustness, to solve the problem of training image database Problems with not fitting to another untrained image database and problems with incomplete and noisy data

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