Content-Based Rotary Kiln Flame Image Retrieval

8/17/2019 Content-Based Rotary Kiln Flame Image Retrieval

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Content-Based Rotary Kiln Flame Image Retrieval

Zhang Hongliang1, Chen Xiangtao2, Zou Zhong1, Li Jie1

School of Metallurgical Science and Engineering, Central South University, Changsha, Hunan 410083,

ChinaSchool of Information Science and Engineering, Central South University, Changsha, Hunan 410083,

ChinaCorresponding Author ：Chen Xiangtao ([email protected])

Abstract

In this paper, a content-based image retrieval system for

rotary kiln flame image (CBIR-RKFI) is introduced for the

purpose of making good use of rotary kiln flame images. Itcalculates the texture and fire & clinker features of the

flame image, then, through the similarities comparison, it

returns a set of original retrieval results. Moreover, with

the users’ relevant feedbacks, it optimizes the final retrievalresults. A prototype system was realized based on the rotary

kiln image database that contained more than 500 rotary

kiln flame images (Sampled in an alumina rotary kiln). Re-

trieval experiments with different features were carried out.

The results demonstrate the effectiveness of the retrieval

methods, and among them, the integrated features based

method has the highest precision (84% ). The research can

provide strong support for modern rotary kiln supervision

and management.

Key Words: rotary kiln flame image, content-based image

retrieval, texture features, relevance feedback, fire &

clinker features

1. Introduction

The traditional control of rotary kiln is usually per-

formed based on the “watch flame” by experienced opera-

tors. This is a subjective and low efficient job. Recently,with the development of rotary kiln automation, computersoftware and hardware technology and the digital image

process technology, the “Computer watch flame” is replac-ing the traditional “watch flame” by experienced worker.

As a consequence, mass digital flame image data, which is

important for rotary kiln control and management, will be produced every day. Therefore, how to effectively manage

and utilize these image data becomes a challenging and

difficult research issue. It will be of great important for ro-

tary kiln management and control.

The traditional database management systems (DBMS),

which are keywords based query, aren’t the efficient imageretrieval methods: first, unlike the traditional text data, the

digital image data can’t be fully described only by some

keywords; second, the procedures of description are time-

consuming and subjective. Therefore, the traditional imageretrieval methods have low precision and are not suitable

for digital image retrieval. While, the content-based image

retrieval (CBIR) is to calculate the similarities between the

images and return a group of similar results. It has become

the main method for image retrieval. At present, it has been

studied in many fields[1-2], such as: medical image data

retrieval [3], human face image database retrieval [4], traf-

fic tools image database retrieval and others [5]. Some of

them have been applied in practice and achieved very good

performances. However, in the rotary kiln industry, due tothe low automation level and other reasons, none studieshave been made on the rotary kiln flame image retrieval.

Therefore, in this paper we proposed a content-basedimage retrieval system for rotary kiln flame image. For the

queried image, we calculated its texture and fire & clinker

features to form the query vector, then calculated the simi-

larities between all the images in image database and the

queried one, and get the original result according to the

similarities comparison. Moreover, with the users’ rele-

vance feedbacks, update the query vector and get the final

retrieval results. Experimental results indicate the effective-

ness of the proposed system.The rest of the paper is organized as follows. Section 2

presents the extraction of the features. In Section 3, we de-

scribe the similarity calculation and relevant feedback while

Section 4 explains the system architecture. Experiments and

performance evaluation are given in Section 5. Finally,concluding remarks are drawn in Section 6.

2. Feature extraction

The flame image is differ from the other images in thefollowing ways: first, all the flame images are sampled

from the same rotary kiln and they are similar in some way;

second, the main objects in the flame images are clinker andfire shape. So, in order to gain the overall image informa-tion, four texture features were extracted. At the same time,

according to the expertise of “flame watch” operators, the

features of the fire & clinker were also extracted, such as

the color, the fire length and clinker height, because the

2008 Congress on Image and Signal Processing

978-0-7695-3119-9/08 $25.00 © 2008 IEEE

DOI 10.1109/CISP.2008.578

490

2008 Congress on Image and Signal Processing

978-0-7695-3119-9/08 $25.00 © 2008 IEEE

DOI 10.1109/CISP.2008.578

490


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color indicate the temperature of the fire and the clinker,

while the latter reflect the combustion states inside the kiln.

They represent the key features of the main content that the

operators should acquire when watching the flame. One

thing must be noted is that the features of the queried image

are calculated on-line, while the features of images in database are calculated off-line and saved into database.

2.1. Texture features extraction

Although there aren’t standard definitions about the tex-

ture, but it provides the features such as smoothness, rough-

ness and orderliness. In image processing, there are three

kinds of methods to calculate texture: statistical, structural

and frequency spectrum method [6]. In this paper, the statis-tical method is adopted, which means that the texture fea-

tures are calculated by Gray Level Co-occurrence Matrix

(GLCM) [7-8].

The GLCM is a matrix represented by probabilityP( g 1, g 2) and reflected the probability of the (d, θ ) apart

points, ( x1, y1) and ( x2, y2), whose gray-value are g 1 and g 2

respectively. That is:1 1 2 2 1 1 1 2 2 2

1 2

#{[( , ),( , )] | ( , ) & ( , ) }( , )

#

x y x y S f x y g f x y g P g g

S

∈ = == (1)

Where f is the gray-value function; g 1 and g 2 are the im-age gray-values; symbol “#” is the element number of the

aggregate; S is the whole image area. The GLCM reflectsthe gray-value information in direction, neighbour intervaland scope. It is the foundation of their arrangement.

In this paper, we selected d=1, θ =0º according to the

experiment results. The feature 1-4 are calculated as follow-

ings:

Entropy:

1 2

1 1 2 1 2( , ) log( ( , ))

g g

f P g g P g g = −∑∑ (2)

Energy: [ ]1 2

2

2 1 2( , ) g g

f P g g = ∑∑ (3)

Inverse differential moment：

1 2

3 1 22

1 2

1( , )

1 ( ) g g f P g g

g g =

+ −∑∑ (4)

Contrast:

1 2

2

4 1 2 1 2( ) ( , ) g g

f g g P g g = −∑∑ (5)

2.2. Fire & clinker features extraction

Since the sampling camera is fixed and the rotary kiln is

in stable state, the fire and clinker area of flame image will

be appeared in a fixed area. So according to expertise, wecan define the reference point ( P 0 in Fig.1 and Fig.2) and

the axial of the fire (l 0 in Fig.1) for the calculation.

2.2.1. Fire features calculation. As one of the main objects

in rotary kiln flame images, the fire information is a basic

criterion for flame image classification. The features of fire

include color and shape: the color will be connected with

the combustion temperature and the shape will reflect the

combustion status information. In this paper, following fire

features are extracted: average gray-value, fire length and

fire offset degree.Feature 5: Average gray-value of the fire area f 5. We can

define and segment the fire area according to the expertise,and calculate the average gray-value of the pixels that be-

longs to the fire using the following formula:

5

,

1( , )

F i j S F

f f i j N

∈

= ∑ (6)

Where, f (i, j) is the image function, (i, j) is the point in

image plane, N F is the number of pixels that belongs to fire,

S F is the segmented fire area.

Feature 6: As shown in Fig.1, where l 1 is the line per-

pendicular to l 0 and pass through P 0, where l 2 is the line

perpendicular to l 0 and pass through P i. If d i is the maxi-mum distance then the fire length f 6 is d max ,and the fire off-

set f 7 is d max/|P max ,P 0|.

Fig.1 Fire Length Extraction

2.2.2. Clinker features extraction. The clinker is the other

object in rotary kiln, which is the production of rotary kiln.

In this paper, the features we used are clinker average gray-value and clinker height. The former is concerned with the

clinker temperature and the latter is related with clinkerstatus, which indicates whether the clinker has satisfied the

requirement.

Feature 8: Average gray-value of clinker area f 8. We de-

fine the “approximate clinker area”, where the clinker willappear, according to the expertise. Then we segment clinker

from this area, and finally calculate the average gray-value

of the pixels that belong to clinker with following equation:

8

,

1( , )

M i j S M

f f i j N ∈

= ∑ (7)

Where N M is the number of pixels after segmentation that

belong to clinker; S M is the clinker area after segmentation.Feature 9: Clinker height, which means the height that

the clinker is raised by the rotary kiln as shown in Fig.2,

where l 0 is the base line of the clinker. The clinker height

feature f 9 is the distance ( H clinker ) between the top point P maxand the base line l 0.

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Fig.2 Clinker Height Extraction

2.2.3. Gray feature for whole image. Feature 10: the greyfeature of the whole flame image f 10. It can be calculated by

followings:

10

,

1( , )

IMGi j S IMG

f f i j N

∈

= ∑ (8)

Where, N IMG is the number of pixels of whole image;

S IMG is the whole image area.

3. Similarity calculation and relevant feedback

The system follows the nearest neighbour search rule

and uses a simple Euclidean distance between features vec-tor F q of query image and F j of image in database. How-

ever, we must realize that since each feature has identicalmeaning, its amplitude will differ greatly, so we have to

normalize them.

3.1. Features normalization

Suppose that there are N frames of flame images in data-

base, then for the ith feature f i of all the images, a matrix

can be formed:1 2[ , , , , , ]i i i ij iN F f f f f = … … . If the maxi-

mum and minimum values are MAX F and MIN F , then the ith

feature of jth

image can be normalized into [0, 1] according

the following equation:

'ij F

ij

F F

f MIN f

MAX MIN

−

=

−

(9)

3.2. Similarity calculation between feature vectors

Given the feature vectors for queried image and database

images are:

1 2, , ,

q q q qM F f f f = … ; 1 2, , , j j j jM F f f f = … (10)

Then the similarity SIM ( F q, F j) can be calculated with the

following Euclidean distance:

2

1

( , ) ( ) M

q j qi ji

i

SIM F F f f =

= −∑ (11)

3.3. Relevance feedback

The aim of relevance feedback is to optimize the re-

trieval results according to the user requirement through

man-machine interaction. Recently, most studies are con-centrated on two aspects: query point movement and re-

weighting [9-10]. In this paper, we use the former method

(Rocchio algorithm [11]) to adjust the query point. It can be

described as following equation:

1

1 1* * *

R IR

i i i i

i D i D R IR

F F D D N N

α β γ +

∈ ∈

= + −

∑ ∑ (12

)

Where, F i and F i+1 are the query vector in the ith

and(i+1)

th query; Di is feature vector; D R and D IR are the posi-

tive feedback(relevant feedback) and negative feedback

(irrelevant feedback) and N R and N IR are the total numbers

of the corresponding images. The relevance of an image is

determined by the experienced kiln operators with the help

expert knowledge; α, β and γ are the parameters controllingthe relative weight of each component. In this paper, we

selected α=0.5, β =0.3 and γ=0.2 based on the experiment

results.

4. System architecture

Our proposed image retrieval system is composed of the

following modules: Graphics User Interface module-provides the graphics

user interface and displays retrieval results;

Features Extraction Module-extracts the low-level

features including texture and fire & clinker features;

Similar Calculation and Relevant Feedback-calculates

the distance between two images and return the simi-

larity; with labelled of the retrieval images with “rele-

vant” or “irrelevant” by user, iterates the retrieval

process; Image Database Module-since there isn’t a standard

rotary kiln flame image database, we construct one

with the flame images sampled from rotary kiln.

The framework of our proposed CBIR-RKFI system is

depicted in Fig.3. The main processing of the system in-volves the offline and online stages. Offline processing in-cludes feature extraction, representation, and organization.

Online processing is the interaction between the user and

the system through the Graphics User Interface (GUI). The

online processing steps are described as follows:

Step 1: Initial query

User can browse through the image collection and inputthe initial query image to the system, which will calculate

the feature vector of the queried image. The system follows

the nearest neighbour search rule and uses a simple Euclid-

ean distance measure for matching the query with the im-

ages in the database, and subsequently returns the 10 most

similar images.

Step 2: Relevance feedback The user provides his/her evaluation by labelling each

displayed image with “relevant” or “irrelevant”. Based on

the current feedback images, a new query vector is created

and a new ranked list of images which better approximate

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the user’s preferences is obtained through the retrieval

process. The newly retrieved result is presented to the user.

Step 3: EndStop if the user is satisfied with the retrieval result; oth-

erwise, repeat step 2.

5. Experiments and performance evaluation

The system prototype in this paper is designed by

VC++6.0, and the rotary kiln flame image database is real-

ized by Microsoft SQL Server 2000. Since there isn’t a

standard flame image database, we built an experimental

database in this work. The raw image data is sampled byCCD (Charge Coupled Device) and digital image sampling

card (See hardware list in Tab.1) in the head of the rotary

kiln of an alumina plant. 500 gray scale images, whose sizeis unified into be 768×576 pixels, are selected randomly

and stored into the image database in this paper. They were

pre-processed and extracted features, and then saved into

image database.

Fig.3 Framework of the proposed CBIR-RKFI system

Tab.1 Hardware List of Image Sample

Hardware Type and Configuration

CCD SC-4183BRH color 1/3"SONY CCD 0.8 LUX

Water-cooling Shield SL-I

Image Sample Card MicroView V5.0Image Process Plat-

form

Windows XP SP2; CPU:

2.20G; Memory: 512M

5.1. Experiments and results

In order to evaluate the system’s performance, four kinds

of experiments are carried out:

Flame image retrieval based on fire & clinker fea-

tures without relevance feedback;

Flame image retrievals based on fire & clinker fea-

tures with relevance feedback;

Flame image retrieval based on integrated features(texture fire & clinker features) without relevance

feedback;

Flame image retrievals based on integrated featureswith relevance feedback.

During the experiments, we firstly selected a randomflame image (The “Queried Image” displayed in Figure4) to

carry out the upper four retrievals. The results are shown in

Fig.5 and Fig.6.

Then, choose 10 images randomly as the query images

to perform experiment 2 and 4, and use the following preci-

sion measurement to evaluate the retrieval performance:

1

1 T

avg i

iq

P P N

=

= ∑ (13)

Where N q is the number of selected queries, and in this

paper, N q =10. P i is precision defined by:

Number of relevant retrieved images

Number of retrieved imagesi P =

(14)

We calculated the average precisions of the queries withdifferent feedback iterations. The results are displayed in

Fig.7.

Fig. 4-Graphics User Interface of the prototype of

CBIR-RKFI system

(a) (b) (c)

Fig.5 Retrieval results of fire & clinker based queries. a:

Retrieval result without relevant feedback; b: Retrieval re-

sults after the first iteration of relevance feedback; c: Re-

trieval results after the 5th iteration of relevance feedback

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(a) (b) (c)

Fig.6- Retrieval result of integrated features based queries.

a: Retrieval result without relevant feedback; b: Retrieval

results after the first iteration of relevance feedback; c:

Retrieval results after the 5th iteration of relevance feed-

back

Fig.7- Retrieval performance comparison of Fire &

Clinker and Integrated based quires.

5.2. Performance evaluation

In Fig.4-Fig.6, it’s very clear that all the methods can re-

turn a set of images, which are similar to the queried image,

and after relevant feedback, the number of similar images

are increased; as we contrast Fig. 5 with Fig.6, we can find

that the images in Fig.6 (Query by integrated features) aremore similar to the query image, and this is displayed moreclearly in Fig. 7. Moreover, Fig.7 also indicates that the

integrated based queries have higher precision than the fire

& clinker based quires; the precisions increase fast after the

first feedbacks, and then become stable; the highest preci-sion of integrated based queries(more than 83%) is much

higher than that without feedback(lower than 67%). Thus,

we can conclude that the proposed system: integrated fea-

tures based flame image retrieval system with relevant

feedback is effective and can get the satisfactory retrieval

result for rotary kiln application.

However, the proposed relevant feedback is subjectiveand must be depend on the expert knowledge. Therefore,

there should be error between different users. So, based onthis system’s framework, build the semantic model for the

rotary kiln flame image will be interesting for further stud-

ies.

6. Conclusions

In this paper, we present a content-based image retrieval

system for rotary kiln flame image based on fire & clinker

and texture features. We firstly analyzed and extracted the

features from flame images, then combined them with the

relevance feedback information from user to realize the

flame image retrieval. Also, a system prototype is designedwith friendly GUI. Experimental results demonstrate that

our approach is effective in addressing different user infor-mation needs. Therefore, it can provide strong support for

rotary kiln control and management.

AcknowledgmentsThis work was supported by the National Natural Science

Foundation of China (60634020).

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Content-Based Rotary Kiln Flame Image Retrieval