Abstract
A new document image retrieval algorithm is proposed in view of the inefficient retrieval of information resources in a digital library. First of all, in order to accurately characterize the texture and enhance the ability of image differentiation, this paper proposes the statistical feature method of the double-tree complex wavelet. Secondly, according to the statistical characteristic method, combined with the visual characteristics of the human eye, the edge information in the document image is extracted. On this basis, we construct the meaningful texture features and use texture features to define the characteristic descriptors of document images. Taking the descriptor as the clue, the content characteristics of the document image are combined organically, and appropriate similarity measurement criteria are used for efficient retrieval. Experimental results show that the algorithm not only has high retrieval efficiency but also reduces the complexity of the traditional document image retrieval algorithm.
Highlights
With the rapid development of science and technology, society has rapidly stepped into the information stage, and multimedia technology has been widely applied [1]
Due to the narrow image field applicable to the image retrieval technology based on text information, the development of content-based image retrieval technology becomes more important and urgent [2, 3]
Widely used image features mainly include colour, shape, texture, and some spatial relationship of the image. This retrieval technology based on image features overcomes the defects of text-based retrieval methods, greatly improves the retrieval rate and efficiency, and gradually becomes a hot spot in the field of image retrieval
Summary
With the rapid development of science and technology, society has rapidly stepped into the information stage, and multimedia technology has been widely applied [1]. The traditional real wavelet has poor directional resolution, which only includes horizontal, vertical, and diagonal directions, and lacks good translation invariance and directional selectivity It is one of the effective ways to choose a reasonable function model to describe the distribution of wavelet coefficients, most of which are distributed near zero. Literature [8] proposed the construction of a generalized gamma model, and Advances in Mathematical Physics literature [9] applied the Bayesian model to detailed subband amplitude coefficients These models do a good job of describing the distribution of coefficients around the zero mean; from the careful observation and analysis of the wavelet histogram, it can be found that the coefficient density function of the wavelet region does not completely conform to the symmetric distribution. The histogram of wavelet coefficient distribution from some texture images is prominent
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