CCA and 2PKNN based automatic image annotation method

An image tagging and automatic image technology, applied in character and pattern recognition, special data processing applications, instruments, etc., can solve problems such as category imbalance, weak tags, automatic image tagging semantic gap, etc.
CN105808752AActive Publication Date: 2016-07-27DALIAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
DALIAN UNIV OF TECH
Publication Date
2016-07-27

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Abstract

The invention belongs to the sub-field of the learning theory and application in the technical field of computer application, and relates to a CCA and 2PKNN based automatic image annotation method, in order to solve problems of a semantic gap, a weak annotation, and category imbalance that exist in an automatic image annotation task. The method comprises: firstly, for a semantic gap problem, mapping two features to a CCA sub-space, and solving a distance between the two features in the sub-space; for a weak annotation problem, establishing a semantic space for each annotation; for a category imbalance problem, by combining a KNN algorithm, finding out k nearest neighbors of a test image in the semantic space of each annotation, constituting the k nearest neighbors to an image sub-set, and by using a visual distance between the sub-set and the test image, and by combining a Bayesian formula, assigning a few annotations with the highest score to the test image; and finally, optimizing an image annotation result by using correlation between annotations. The method disclosed by the present invention has a greater degree of improvement for image annotation performance.
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Description

technical field

[0001] The invention belongs to the subfield of learning theory and application in the field of computer application technology, and the invention focuses on the problem of automatic image labeling. An automatic image annotation method based on CCA and KNN is proposed to solve the problems of semantic gap, weak labeling and category imbalance in automatic image annotation tasks. First, a total of 15 global and local features are extracted for each image, and the distance between the underlying feature and the high-level semantics is obtained by using CCA for each feature, and the above-mentioned distances are fused to form the final distance, which solves the problem of semantic gap. According to the distance, the semantic space of each label can be obtained, and the original labeled images of each label can be combined to form a more complete semantic space. Solved the weak tagging issue. For each label, select k neighbors from its semantic space using the K...

Claims

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