Energy model based image semantic annotation method

A technology of semantic annotation and energy model, which is applied in semantic analysis, image analysis, image data processing, etc., can solve the problems of reduced annotation accuracy, semantic confusion, and failure to consider spatial constraints, so as to avoid semantic confusion and improve accuracy.

Inactive Publication Date: 2014-10-01
ZHEJIANG UNIV
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Therefore, only the constraint relationship between semantics (that is, labels) is considered, and the spatial constraint relatio

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  • Energy model based image semantic annotation method
  • Energy model based image semantic annotation method
  • Energy model based image semantic annotation method

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Embodiment Construction

[0078] The present invention will be described in detail below in conjunction with specific embodiments.

[0079] An image semantic annotation method based on an energy model, comprising:

[0080] (1) Divide the original image into several regional images, and extract the visual feature vectors of each regional image.

[0081] In this embodiment, the visual feature fuzzy c-means (FCM) clustering algorithm is used to segment the original image and extract the visual feature vectors of the images in each region. The visual feature vector can be a feature vector based on the Moving Picture Experts Group-7 (MPEG-7) feature, or a feature vector based on the scale-invariant feature transform (Scale-invariant feature transform, SIFT) A feature vector, in this embodiment, is a feature vector based on SIFT features.

[0082] (2) According to the visual feature vector of each region, use the trained SVM classifier to determine the candidate semantic labels of each region image, and th...

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Abstract

The invention discloses an energy model based image semantic annotation method. A full energy function of an original image under different regional image and semantic tag corresponding relationships is built according to regional image and semantic tag corresponding potentials and interaction potentials between semantic tags and the infusion is performed on the context information and the exterior information of the image through the full energy function. Compared with context object classification models in the prior art, wherein only the co-occurrence information between objects are utilized or only the fixed spatial relations and the simple frequency count methods are utilized in the context object classification models, the spatial structure information between the objects is fully utilized through the fuzzy spatial relations due to the full energy function and accordingly the semantic chaos is effectively avoided and the accuracy of the semantic annotation is improved.

Description

technical field [0001] The invention relates to the technical fields of image retrieval and automatic image labeling, in particular to an image semantic labeling method based on an energy model. Background technique [0002] In order to classify image scenes semantically, objects in the segmented regions need to be annotated after image segmentation. Object annotation in image segmentation regions directly affects the accuracy of scene semantic understanding and classification. Many researchers have carried out object labeling work in images, basically using the underlying visual features of image regions to classify objects. In recent years, researchers have carried out object recognition with fusion of context information, but its accuracy has not met the actual needs and needs to be improved. [0003] In order to solve the problem of combining contextual information into an object classification framework, machine learning techniques are generally borrowed to fuse visua...

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Application Information

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IPC IPC(8): G06F17/30G06K9/66G06K9/62G06T7/00
CPCG06F40/30G06F18/29G06F18/24
Inventor 姚敏赖盛章李昌英吴朝晖
Owner ZHEJIANG UNIV
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