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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.

Active Publication Date: 2016-07-27
DALIAN UNIV OF TECH
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is that due to problems such as semantic gap, weak labeling, and category imbalance in automatic image annotation, it is impossible to balance efficiency and accuracy. An automatic image annotation method based on CCA and 2PKNN is proposed

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  • CCA and 2PKNN based automatic image annotation method
  • CCA and 2PKNN based automatic image annotation method
  • CCA and 2PKNN based automatic image annotation method

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Embodiment Construction

[0051] Embodiments of the present invention will be described in detail below in combination with technical solutions and accompanying drawings.

[0052] 1. Determine the data sets. The present invention selects three standard image annotation data sets, which are respectively Corel5k, ESPGame, and IAPRTC-12. For the Corel5k dataset, which includes 4999 images and 260 labels, 4500 images are selected as the training set, and the rest are used as the test set. For the ESPGame dataset, it includes 20770 images with 268 labels. Among them, 18689 images are selected as the training set, and the rest are used as the test set. For the IAPRTC-12 dataset, it includes 19627 images with 291 labels. 17665 of them are selected as the training set, and the rest are used as the test set.

[0053] 2. Feature extraction and normalization. For each image, extract its global features and local features. The global features include GIST, RGB, Lab and HSV color histograms. Local features incl...

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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.

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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/5866G06F18/2413
Inventor 孙亮王雪莲葛宏伟谭国真
Owner DALIAN UNIV OF TECH
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