Texture image retrieving method based on truncation generalized Cauchy modeling

Abstract

The invention discloses a texture image retrieving method based on truncation generalized Cauchy modeling. The method comprises the following steps: firstly executing the non-subsample wavelet transform to the texture image; secondly, executing the statistical modeling to an HSB coefficient obtained by the transform by using a truncation generalized Cauchy distribution probability density function; and executing the parameter estimation to a position parameter (as shown in the specification) and a scale parameter (as shown in the specification); finally, using the formula as shown in the specification and the formula as shown in the specification as image features, and applying to the image retrieval. An experimental result shows that the method is capable of, because the estimated position parameter (as shown in the specification) and scale parameter (as shown in the specification) of the truncation generalized Cauchy distribution are used as the features of each image, effectively reducing the image feature dimension, reducing the similarity calculation time, improving the retrieval efficiency, and reinforcing the algorithm practicability.

Description

technical field [0001] The invention belongs to the technical field of digital image retrieval, relates to a content-based image retrieval method, in particular to a texture image retrieval method based on truncated generalized Cauchy modeling. Background technique [0002] At present, the popularization of electronic devices such as smart phones and digital cameras enables people to capture images anytime and anywhere to form image information, and instantly disseminate them on the Internet. Digital images are growing at an alarming rate. The problem today is how to carry out the process in the huge image database. Retrieve questions efficiently and accurately. [0003] The early traditional text-based image retrieval method (TBIR) is a retrieval process based on word matching, which searches images with various annotations (such as names, numbers, content brief descriptions, etc.). With the gradual expansion of the amount of image information, TBIR technology encounters s...

AI Technical Summary

Problems solved by technology

With the gradual expansion of the amount of image information, TBIR technology encounters severe problems: text annotation is difficult to describe more and more abundant image content in an all-round and accurate manner, and content-based image retrieval technology (CBIR) emerges as the times require.

[0004] The feature extraction of texture images is the key to determine the quality of a CBIR algorithm. The primary task of CBIR technology is to find a better method of feature extraction, extract unique and accurate features, and lay the foundation for searching for accurate images. However, the existing CBIR The technical algorithm is relatively cumbersome, difficult to implement, and time-complex. Therefore, finding a CBIR technology with low complexity, fast operation speed, and good feature extraction effect is a topic that needs in-depth research at present.

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