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Automatic image marking method emerged with pseudo related feedback and index technology

A pseudo-correlation feedback and automatic image technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as inability to obtain, lack of text characteristics of tagged words, and limit the accuracy of tagged word sets, so as to improve performance Effect

Inactive Publication Date: 2007-11-21
BEIJING JIAOTONG UNIV
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Problems solved by technology

[0014] However, in the two existing tagging methods for fusion retrieval, simple initial retrieval is performed, which severely limits the accuracy of the set of related tagged words and restricts the improvement of tagging performance.
Analyzing its internal reasons, the existing simple retrieval technology cannot obtain more relevant images; moreover, the sorting of tagged words is also one-time, and it lacks a good use of the text characteristics of tagged words

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  • Automatic image marking method emerged with pseudo related feedback and index technology
  • Automatic image marking method emerged with pseudo related feedback and index technology
  • Automatic image marking method emerged with pseudo related feedback and index technology

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Embodiment Construction

[0026] Relevant technical content and detailed description of the present invention, now cooperate accompanying drawing to explain as follows:

[0027] Fig. 1 is a framework model diagram of the automatic image labeling method of the fusion of pseudo-correlation feedback and retrieval of the present invention. As shown in the figure, step 1: set the unlabeled image I q To query an image, search in the labeled image library to find the k nearest neighbor images, which constitute the set of related images Ψ q and the tagged word set Ψ for related images w . Assuming that the top few images are related images, use I r express. Related Images Ir with the query image I q The similarity measure p(I r |I q ) is given by:

[0028] p(I r |I q ) = ω r ·S(I r |I q ) (1)

[0029] (a)S(I r |I q ) is the relevant image I r with the query image I q The similarity between them is defined as follows:

[0030] S(I r |I q )=exp(-D(I r ,...

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Abstract

An automatic image-labeling method of fusing pseudo-correlation feedback with index technique includes indexing unlabeled query image in labeled imagebank to obtain k numbers of correlation images and label word set of correlation image, calculating posterior probability (PP) of each labeled word labeled query image (ELWQI), picking up mean value vector as new query vector according to k numbers of correlation image, repeating said steps till maximum iteration frequency N is obtained, calculating stability factor (SF) of each label word in each cycle of labeled word set, calculating sequence probability of ELWQI according to PP and value of SF in order to label query image.

Description

technical field [0001] The invention relates to a novel automatic image labeling method, in particular to an automatic image labeling method which combines pseudo-correlation feedback and retrieval technology. Background technique [0002] With the emergence of a large number of multimedia, its effective management and retrieval has become an important research topic. In the 1970s and 1980s, image retrieval was mainly based on text retrieval, and the text related to images was manually annotated, which required a large workload and depended on the individual subjective judgment of the annotator. In 1992, the content-based image retrieval (CBIR-Content Based Image Retrieval) technology was first proposed, which can automatically extract the underlying features of images and automatically retrieve them. There are many CBIR systems that have been developed. However, due to the existence of the "semantic gap", the accuracy of the retrieval results is not high, which cannot mee...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 赵耀赵玉凤朱振峰
Owner BEIJING JIAOTONG UNIV
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