CN102663422A - Floor layer classification method based on color characteristic - Google Patents

Abstract

The invention discloses a floor layer classification method based on a color characteristic. In a HSV color space, extraction of a floor color moment characteristic is performed on tone and saturation components, a high weight is given to tone characteristic data and an influence of a texture characteristic on the classification is reduced. A brightness component is removed. The influence of an industrial on-site illumination condition on the floor is avoided. When carrying out on-line classification on a unknown floor sample, determination is performed on the unknown floor sample from coarse to fine layer by layer through using a offline multilayer classification basis database which is established in advance and adopting a K nearest adjacent-shortest distance decision. The method of the invention has the following advantages that: a floor manufacturer can accurately classify floor classes; floor production efficiency can be increased and artificial cost can be reduced.

Description

Floor hierarchy classification method based on color characteristic

Technical field

The present invention relates to a kind of floor hierarchy classification method, be applied to the floor industry spot classification of new production floor sample is judged based on color characteristic.

Background technology

Along with rapid economy development, the demand on floor is also more and more, and the floor is categorized into the problem that presses for solution for fast and accurately in production run.At present, the classification on floor realizes the main artificial visually examine of leaning on, and its artificial factor is bigger, therefore utilizes Digital image processing technique to be classified in the floor and more can effectively guarantee the accuracy of floor classification.

Because the floor color is reflection floor surface vision and a psychosensorial key character, with the evaluation of classification substantial connection is arranged, thereby make and in recent years the classification processing based on color characteristic has all been carried out certain research.As wear rainbow etc. through main color characteristic, utilize RBF network, K nearest neighbor and arest neighbors in the neural network that the wood sample image is carried out classification processing respectively; Wang Ke very waits the sort research that utilizes even color space that the color characteristic of timber is measured and distinguished.

Summary of the invention

The objective of the invention is to improve the automaticity and the rapidity thereof of classification processing in the production run of floor; A kind of floor hierarchy classification method based on color characteristic is provided; Effectively raise the accuracy of floor production firm to the floor category classification; Both improve floor production efficiency, reduced cost of labor again.

According to technical scheme provided by the invention, said floor hierarchy classification method based on color characteristic may further comprise the steps:

(1) coloured image according to the floor sample extracts the color statistical nature;

(2), set up the multi-level floor of off-line sample classification according to the storehouse according to the color characteristic information of known floor sample;

(3) when unknown classification floor sample is carried out online classification, utilize the bee-line classifying rules, ask for the similar classification of unknown floor sample according to given in advance off-line classification foundation storehouse; Utilize K nearest neighbor-bee-line method, ask for the accurate classification of unknown floor sample according to given in advance off-line classification foundation; Be specially:

(3.1) when the similar classification of unknown floor sample: calculate the distance of the color moment proper vector after the unknown sample proper vector merges to all similar classifications, and to the processing of sorting of all distances; Will with the difference of bee-line less than sample ultimate range D _MaxThe similar classification of all of/4 all reduction to the classification process of next level;

(3.2) when the accurate classification of unknown floor sample: at first ask for unknown floor sample and affiliated sample big type in the distance of each sample, and by the distance size to the processing of sorting of each sample in big type of the sample; Add up the pairing floor of top n distance sample classification number then, ask for classification number maximal value k _Max, judge k _MaxWhether unique; If unique, then unknown floor is referred to k _MaxIn the pairing classification, classification processing finishes; If not unique, then ask for floor to be measured and all k _MaxThe distance of identical floor sample, with unknown floor sample be referred to its floor sample apart from minimum in.

Said extraction color statistical nature is to utilize the color moment of the coloured image of floor sample to extract the color statistical nature information of floor sample, removes luminance component to reduce the influence of illumination condition to classifying quality; And give tone component and the different weighting proportion of saturation degree component.

When setting up said floor sample classification, at first determine the color moment of each floor sample according to given floor sample according to the storehouse; Through minimax distance classification algorithm each floor sample is carried out clustering processing then, color moment close in value classification is merged to together, ask for the data mean value of similar classification floor color sample square; The minimax distance algorithm calculates ultimate range between all samples as the reference of sorting out threshold value, if certain sample to a distances of clustering centers less than ultimate range D _Max1/2, then be included into such, otherwise set up new cluster centre.

Advantage of the present invention is: the present invention selects the color moment characteristic for use to the expression mode of floor color characteristic, compares with other expression modes, can more simple and effectively express the colouring information on floor; Utilize tone and saturation degree component to ask for the color moment characteristic on floor at the HSV color space, reduce illumination the accurately influence of row of floor classification; Set up the multi-level floor of off-line sample classification according to the storehouse, improve the accuracy of floor, position sample classification; When the pairing approximation classification was judged, the approximate classification of all that will close on was included into the judgement of next stage equally, improved the accuracy of classification; Utilize K nearest neighbor-bee-line method that the unknown floor accurate classification of sample is judged, further guarantee the accuracy of floor classification.

Description of drawings

Fig. 1 is overall implementation framework figure of the present invention.

Fig. 2 is that the floor sample classification is according to the storehouse synoptic diagram.

Fig. 3 is unknown floor sample classification realization flow figure.

Embodiment

In order to improve the automaticity of unknown floor sample classification in the production run of floor, the present invention has developed a kind of multi-level floor online classification method based on the color statistical nature.Floor sorting technique algorithmic code amount is little, fast operation, high, real-time, the good stability of precision, can effectively improve unknown floor sample classification effectiveness, reduce production costs.

Below in conjunction with accompanying drawing and embodiment the present invention is described further.

This is based on the floor hierarchy classification method of color characteristic; It is as shown in Figure 1 that unknown floor sample is carried out the online classification implementation procedure; At first set up multi-level off-line floor sample classification according to the storehouse, and then carry out online classification successively and realize unknown floor sample is from coarse to fine according to known floor sample.

Step is following:

1 coloured image according to the floor sample extracts the color statistical nature;

2 set up the multi-level floor of off-line sample classification according to the storehouse according to known floor sample;

3 when carrying out online classification to unknown classification floor sample, utilizes the bee-line classifying rules, asks for the similar classification of unknown floor sample according to given in advance off-line classification foundation storehouse; Utilize K nearest neighbor-bee-line method, ask for the accurate classification of unknown floor sample, recomputate the characteristic of approximate classification under sample characteristic data and the sample of classification under the sample of unknown floor at last according to given in advance off-line classification foundation.

More than the 1st step extract specifically being embodied as of color statistical nature:

When extracting the color statistical nature of floor sample, select for use color moment to represent the color statistical nature of floor sample.The form of the mathematic(al) representation of three low order squares of color moment characteristic is:

${\mathrm{\μ}}_i=\frac{1}{n}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{h}_{\mathrm{ij}}$

${\mathrm{\σ}}_i={\left(\frac{1}{n}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{(h_{\mathrm{ij}}-{\mathrm{\μ}}_i)}^2\right)}^{1/2}$

$s_i={\left(\frac{1}{n}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{(h_{\mathrm{ij}}-{\mathrm{\μ}}_i)}^3\right)}^{1/3}$

In the formula, h _IjRepresent that gray scale is the probability of the pixel appearance of j in the i Color Channel, n representes number of greyscale levels.

Select for use 2 chrominance properties values (tone, saturation degree) to extract its relevant colors moment characteristics and handle, remove the property value of brightness with the classification and Detection of carrying out the floor.

On the tolerance of similarity between the floor, need to give the higher weight of tone characteristics data allocations, distribute lower weight for the saturation degree characteristic, reduce the influence of texture features the floor classification.

The weighted euclidean distance of different floors color moment vector can be expressed as

d＝ω ₁(x _H-x′ _H)+ω ₂(x _S-x′ _S)

Wherein, ω ₁, ω ₂The weights of representing tone color moment characteristic and saturation degree color moment characteristic respectively.

The 2nd step was set up the multi-level floor of off-line sample classification specifically being embodied as according to the storehouse:

At first determine the color moment of each floor sample according to given floor sample; Through minimax distance classification algorithm each floor sample is carried out clustering processing then, color moment close in value classification is merged to together, ask for the data mean value of similar classification floor color sample square.Finally form multi-level floor sample classification as shown in Figure 2 according to the storehouse.

Specifically being embodied as of the 3rd step:

When unknown classification floor sample was carried out online classification, realization flow was as shown in Figure 3.

(3.1) utilize the bee-line classifying rules, the concrete performing step of asking for the similar classification of unknown floor sample according to given in advance off-line classification foundation storehouse is:

Calculate the distance of the color moment vector after the unknown sample proper vector merges to the similar classification of floor sample;

To the ascending processing of sorting of all distances;

Will with the difference of bee-line less than D _MaxThe similar classification of all of/4 all reduction to the processing procedure of next level.

(3.2) utilize K nearest neighbor-bee-line method, when asking for the accurate classification of unknown floor sample, at first obtain k the neighbour of sample x according to given in advance off-line classification foundation.If maximum neighbour's number is k _Max, judge k _MaxWhether it is one; If unique, this classification ω then _MaxUnder x; If a plurality of (as N) ask the neighbour to count k _MaxAffiliated floor sample category feature average and the distance of x:

$d_i={\left[\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(S_i-x_i)}^2\right]}^{\frac{1}{2}};$

Wherein, S is a floor sample category feature average, and n is a number of features.

K nearest neighbor-bee-line method decision rule is:

Use K nearest neighbor-bee-line method to confirm the affiliated accurately implementation procedure of classification in floor to be measured:

1. ask for the distance of each sample in the big class of unknown floor sample and affiliated sample;

2. by distance size to the processing of sorting of each sample in big type of the sample;

3. add up the pairing floor of top n distance sample classification number, ask for classification number maximal value k _Max

4. judge k _MaxWhether unique, uniquely then carried out for the 5. step, otherwise carried out for the 6. step;

5. then unknown floor is referred to k _MaxIn the pairing classification, classification processing finishes.

6. ask for floor to be measured and all k _MaxThe distance of identical floor sample;

7. with unknown floor sample be referred to its floor sample apart from minimum in, classification processing finishes.

Claims (3)

1. based on the floor hierarchy classification method of color characteristic, it is characterized in that, may further comprise the steps:

(1) coloured image according to the floor sample extracts the color statistical nature;

(2), set up the multi-level floor of off-line sample classification according to the storehouse according to the color characteristic information of known floor sample;

(3) when unknown classification floor sample is carried out online classification, utilize the bee-line classifying rules, ask for the similar classification of unknown floor sample according to given in advance off-line classification foundation storehouse; Utilize K nearest neighbor-bee-line method, ask for the accurate classification of unknown floor sample according to given in advance off-line classification foundation; Be specially:

(3.1) when the similar classification of unknown floor sample: calculate the distance of the color moment proper vector after the unknown sample proper vector merges to all similar classifications, and to the processing of sorting of all distances; Will with the difference of bee-line less than sample ultimate range D _MaxThe similar classification of all of/4 all reduction to the classification process of next level;

(3.2) when the accurate classification of unknown floor sample: at first ask for unknown floor sample and affiliated sample big type in the distance of each sample, and by the distance size to the processing of sorting of each sample in big type of the sample; Add up the pairing floor of top n distance sample classification number then, ask for classification number maximal value k _Max, judge k _MaxWhether unique; If unique, then unknown floor is referred to k _MaxIn the pairing classification, classification processing finishes; If not unique, then ask for floor to be measured and all k _MaxThe distance of identical floor sample, with unknown floor sample be referred to its floor sample apart from minimum in.

2. the floor hierarchy classification method based on color characteristic as claimed in claim 1; It is characterized in that; Said extraction color statistical nature; Be to utilize the color moment of the coloured image of floor sample to extract the color statistical nature information of floor sample, remove luminance component to reduce the influence of illumination condition to classifying quality; And give tone component and the different weighting proportion of saturation degree component.

3. the floor hierarchy classification method based on color characteristic as claimed in claim 1 is characterized in that, when setting up said floor sample classification according to the storehouse, at first determines the color moment of each floor sample according to given floor sample; Through minimax distance classification algorithm each floor sample is carried out clustering processing then, color moment close in value classification is merged to together, ask for the data mean value of similar classification floor color sample square; The minimax distance algorithm calculates ultimate range between all samples as the reference of sorting out threshold value, if certain sample to a distances of clustering centers less than ultimate range D _Max1/2, then be included into such, otherwise set up new cluster centre.

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