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Trademark image retrieval method based on multi-scale regional characteristic comparison

An image retrieval and regional feature technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as poor robustness, high missed detection rate, and large impact, and achieve improved robustness and high consistency. The effect of speeding up the retrieval speed

Active Publication Date: 2018-11-20
南昌奇眸科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem solved by the present invention is: the existing trademark image retrieval method is greatly affected by subjective judgment, has high missed detection rate and poor robustness, and there are differences between retrieval results and human visual image judgment

Method used

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  • Trademark image retrieval method based on multi-scale regional characteristic comparison
  • Trademark image retrieval method based on multi-scale regional characteristic comparison
  • Trademark image retrieval method based on multi-scale regional characteristic comparison

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] Image A to be retrieved w×h For example, w and h represent the width and height of the graphic respectively, and the retrieval method of the present invention is used for retrieval.

[0053] First, extract image A w×h And retrieve the features of all images in the system, the specific steps are as follows:

[0054] 1. Customize the specification and sliding step of the multi-scale sliding window. The specification of the sliding window is shown in Table 1. The sliding step μ is 0.1, the horizontal step of the sliding window is 0.1w, and the vertical step of the sliding window is 0.1 h.

[0055]

[0056] Table 1. Specifications of multi-scale sliding windows

[0057] 2. Use the sliding window defined in step 1 as graph A w×h Starting from the upper left corner of , according to the horizontal sliding step and vertical sliding step, slide from left to right and from top to bottom in turn to obtain a system of window image sets R of different sizes, a total of 225, ...

Embodiment 2

[0094] The difference between this embodiment and Embodiment 1 is that the specification of the sliding window and the sliding step are different, see Table 2 for the specific specification, and the horizontal and vertical sliding steps of the sliding window are 0.2w and 0.2h respectively. The similarity distance d of similar window pairs obtained by global inter-scale feature window matching is 0.3, and the offset distance u of the window center position is 0.4; the adaptive threshold matrix T=κ·T 0 ·(s / 100wh) α The κ in α is 0.4, and α is 0.4; the offset distance u of the center position of the similar window obtained by matching the local window features in the ROI is 0.3.

[0095]

[0096] Table 2. Specifications of multi-scale sliding windows

[0097] attached Figure 5 The retrieval results of this embodiment are given, wherein, the graph 000000 is the input graph to be retrieved, and the graphs 000001-000009 are the retrieval results.

Embodiment 3

[0099] The difference between this embodiment and Embodiment 1 is that the specification of the sliding window and the sliding step are different. The specific specification is shown in Table 3. The horizontal and vertical sliding steps of the sliding window are 0.2w and 0.1h respectively. The offset distance u of the window center position of the similar window pair obtained by the feature window matching between global scales is 0.6; the adaptive threshold matrix T=κ·T 0 ·(s / 100wh) α The κ in α is 0.6, and α is 0.8; the offset distance u of the center position of the similar window obtained by matching the local window features in the ROI is 0.3.

[0100]

[0101] Table 3. Specifications of multi-scale sliding windows

[0102] attached Figure 6 The retrieval results of this embodiment are given, wherein, the graph 000000 is the input graph to be retrieved, and the graphs 000001-000009 are the retrieval results.

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Abstract

The invention relates to a trademark image retrieval method based on multi-scale regional characteristic comparison The method comprises the following steps of (1) segmenting an image by using a multi-scale sliding window to obtain a series of windows and then extracting the characteristics of a window image by using a LBP operator, (2) carrying out global inter-scale characteristic window matching on the extracted characteristics to obtain a similar window pair Ai:Bj, (3) eliminating a mismatch in the similar window pair by using a spatial transformation model and screening scale-space consistent similar window, (4) segmenting the screened similar window pair by using an adaptive threshold matrix T to obtain a similar region ROI, (5) performing local window characteristic matching in thesimilar region ROI, and (6) outputting a retrieval result according to a match result of the local window characteristic in the similar region ROI. The ranking of a trademark image retrieval result obtained by the method is consistent with that of a human visual image judgment, the retrieval is fast, and the system robustness is good.

Description

technical field [0001] The invention relates to trademark image retrieval, in particular to a trademark image retrieval method based on multi-scale region feature comparison. Background technique [0002] With the rapid development of science and technology, the popularity of computers and information networks, the amount of various information data is growing at an alarming rate. How to conveniently, accurately and efficiently obtain the required information from the huge information data has become the focus of people's attention. There is a large amount of image information on major social platforms and e-commerce platforms, and image retrieval has become a common retrieval method. Image retrieval can be divided into two categories according to the different ways of describing image content, one is text-based image retrieval (TBIR, Text Based Image Retrieval), and the other is content-based image retrieval (CBIR, Content Based Image Retrieval). Text-based image retrieva...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/46G06K9/62
CPCG06V10/44G06V10/751
Inventor 樊晓东李建圃
Owner 南昌奇眸科技有限公司
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