CN112488134A - Big data image processing method - Google Patents
Big data image processing method Download PDFInfo
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- CN112488134A CN112488134A CN202011512908.2A CN202011512908A CN112488134A CN 112488134 A CN112488134 A CN 112488134A CN 202011512908 A CN202011512908 A CN 202011512908A CN 112488134 A CN112488134 A CN 112488134A
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- 238000003672 processing method Methods 0.000 title claims abstract description 19
- 238000000605 extraction Methods 0.000 claims abstract description 7
- 239000002131 composite material Substances 0.000 claims description 6
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G06F18/22—Matching criteria, e.g. proximity measures
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Abstract
The invention discloses a big data image processing method, which comprises the following steps: s1, presetting processing characteristics, numbering and storing the processing characteristics to a storage module to form a characteristic library; s2, searching a feature library according to requirements to form a processing concentration point; s3, acquiring a target image through the acquisition module, forming a buffer file, and matching and storing the buffer file with the image source address; s4, extracting the features of the target image through an extraction module to form an extracted feature value; s5, performing special matching on the extracted characteristic values and the processing concentration points through a matching module to form matching result values; s6, the matching result value is read by the judging module and is compared and judged with the preset value; s7, if the matching result value is larger than the preset value, the matching result value accords with the processing characteristics, and the matching result value is marked as a related image; and S8, if the matching result value is smaller than the preset value, the matching result value does not conform to the processing characteristics, and the matching result value is marked into an irrelevant image, so that the use convenience and the use efficiency are greatly improved, and the processing precision is ensured.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a big data image processing method.
Background
In the current daily life and work, with the development of internet science and technology and intelligent terminal convergence, the generation speed of images is higher and higher, and in order to ensure the safety and the legality of information propagation, the images need to be processed and separated.
However, most of the existing image processing methods simply perform matching recognition according to the image time and the main features of the image, and only can simply recognize the common sense features, so that the information processing of the brief position of the image is incomplete, not only is misjudgment easily caused, but also the image is easily missed and lost, and therefore a new processing method needs to be proposed.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a big data image processing method.
In order to achieve the purpose, the invention adopts the following technical scheme:
a big data image processing method is characterized in that: the image processing method comprises the following steps:
s1, presetting processing characteristics, numbering and storing the processing characteristics to a storage module to form a characteristic library;
s2, searching a feature library according to requirements to form a processing concentration point;
s3, acquiring a target image through the acquisition module, forming a buffer file, and matching and storing the buffer file with the image source address;
s4, extracting the features of the target image through an extraction module to form an extracted feature value;
s5, performing special matching on the extracted characteristic values and the processing concentration points through a matching module to form matching result values;
s6, the matching result value is read by the judging module and is compared and judged with the preset value;
s7, if the matching result value is larger than the preset value, the matching result value accords with the processing characteristics, and the matching result value is marked as a related image;
s8, if the matching result value is smaller than the preset value, the processing characteristics are not met, and the image is marked as an irrelevant image;
and S9, removing the irrelevant images and storing the addresses and the feature numbers of the relevant images.
Preferably, the preset processing characteristics of step S1 include color, text, size, pixel density and pattern shape of the image.
Preferably, the processing concentration points of step S2 include a single feature matching pattern and a combined feature matching pattern, where the combined feature matching pattern includes a two-feature composite matching and a three-feature composite matching.
Preferably, the feature extraction items of step S4 are consistent with the items of the preset processing features.
Preferably, the step S9 further includes marking the weights of the related images, and arranging the weights in descending order.
According to the big data image processing method provided by the invention, through presetting the processing characteristics, the operation can be carried out according to different departments and requirements, the breadth of the image identification range is effectively improved, then the selection identification is carried out according to different characteristics, the matching precision is effectively improved, omission and misjudgment are avoided, meanwhile, the combination and matching of different characteristics are combined, the purpose is improved, the repeated work is avoided, the resource ratio is optimized, the convenience and the high efficiency of use can be greatly improved, and the processing precision is ensured.
Drawings
FIG. 1 is a schematic block diagram of the system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Examples
A big data image processing method is characterized in that: the image processing method comprises the following steps:
s1, presetting processing characteristics, numbering and storing the processing characteristics to a storage module to form a characteristic library;
s2, searching a feature library according to requirements to form a processing concentration point;
s3, acquiring a target image through the acquisition module, forming a buffer file, and matching and storing the buffer file with the image source address;
s4, extracting the features of the target image through an extraction module to form an extracted feature value;
s5, performing special matching on the extracted characteristic values and the processing concentration points through a matching module to form matching result values;
s6, the matching result value is read by the judging module and is compared and judged with the preset value;
s7, if the matching result value is larger than the preset value, the matching result value accords with the processing characteristics, and the matching result value is marked as a related image;
s8, if the matching result value is smaller than the preset value, the processing characteristics are not met, and the image is marked as an irrelevant image;
and S9, removing the irrelevant images and storing the addresses and the feature numbers of the relevant images.
Preferably, the preset processing characteristics of step S1 include color, text, size, pixel density and pattern shape of the image.
Preferably, the processing concentration points of step S2 include a single feature matching pattern and a combined feature matching pattern, where the combined feature matching pattern includes a two-feature composite matching and a three-feature composite matching.
Preferably, the feature extraction items of step S4 are consistent with the items of the preset processing features.
Preferably, the step S9 further includes marking the weights of the related images, and arranging the weights in descending order.
According to the big data image processing method provided by the invention, through presetting the processing characteristics, the operation can be carried out according to different departments and requirements, the breadth of the image identification range is effectively improved, then the selection identification is carried out according to different characteristics, the matching precision is effectively improved, omission and misjudgment are avoided, meanwhile, the combination and matching of different characteristics are combined, the purpose is improved, the repeated work is avoided, the resource ratio is optimized, the convenience and the high efficiency of use can be greatly improved, and the processing precision is ensured.
Claims (5)
1. A big data image processing method is characterized in that: the image processing method comprises the following steps:
s1, presetting processing characteristics, numbering and storing the processing characteristics to a storage module to form a characteristic library;
s2, searching a feature library according to requirements to form a processing concentration point;
s3, acquiring a target image through the acquisition module, forming a buffer file, and matching and storing the buffer file with the image source address;
s4, extracting the features of the target image through an extraction module to form an extracted feature value;
s5, performing special matching on the extracted characteristic values and the processing concentration points through a matching module to form matching result values;
s6, the matching result value is read by the judging module and is compared and judged with the preset value;
s7, if the matching result value is larger than the preset value, the matching result value accords with the processing characteristics, and the matching result value is marked as a related image;
s8, if the matching result value is smaller than the preset value, the processing characteristics are not met, and the image is marked as an irrelevant image;
and S9, removing the irrelevant images and storing the addresses and the feature numbers of the relevant images.
2. A big data image processing method according to claim 1, characterized in that: the preset processing characteristics of the step S1 include color, text, size, pixel density, and pattern shape of the image.
3. A big data image processing method according to claim 1, characterized in that: the processing concentration point of the step S2 includes a single feature matching pattern and a combined feature matching pattern, where the combined feature matching pattern includes two-term feature composite matching and three-term feature composite matching.
4. A big data image processing method according to claim 1, characterized in that: the feature extraction items of the step S4 are consistent with the items of the preset processing features.
5. A big data image processing method according to claim 1, characterized in that: the step of S9 further includes labeling the weights of the related images and arranging them in descending order.
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| CN202011512908.2A CN112488134A (en) | 2020-12-20 | 2020-12-20 | Big data image processing method |
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| CN202011512908.2A CN112488134A (en) | 2020-12-20 | 2020-12-20 | Big data image processing method |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107330859A (en) * | 2017-06-30 | 2017-11-07 | 广东欧珀移动通信有限公司 | A kind of image processing method, device, storage medium and terminal |
| CN107609561A (en) * | 2017-08-01 | 2018-01-19 | 佛山市深研信息技术有限公司 | A kind of big data image processing method |
| CN108304805A (en) * | 2018-02-01 | 2018-07-20 | 广东聚晨知识产权代理有限公司 | A kind of big data image recognition processing system |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107330859A (en) * | 2017-06-30 | 2017-11-07 | 广东欧珀移动通信有限公司 | A kind of image processing method, device, storage medium and terminal |
| CN107609561A (en) * | 2017-08-01 | 2018-01-19 | 佛山市深研信息技术有限公司 | A kind of big data image processing method |
| CN108304805A (en) * | 2018-02-01 | 2018-07-20 | 广东聚晨知识产权代理有限公司 | A kind of big data image recognition processing system |
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Application publication date: 20210312 |