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PAPER

Special Issue on Machine Vision Applications

A Multi-Resolution Image Understanding System
Based on Multi-Agent Architecturefor High-Resolution Images
Keiji YANAI† and Koichiro DEGUCHI†† , Regular Members

SUMMARY
Recently a high-resolution image that has morethan one million pixels is available easily. However, such an image
requires much processing time and memory for an image understanding system. In this paper, we propose an integrated image understanding system of multi-resolution analysis and multiagent-based architecture for high-resolution images. The system
we propose in this paper has capability to treat with a highresolution image eﬀectively without much extra cost. We implemented an experimental system for images of indoor scenes.
key words: multi-resolution analysis, multi-agent, image understanding system, scene interpretation

1.

Introduction

Recently a high-resolution image that has more than
one million pixels is available easily due to highperformance digital cameras. However, in researches ofimage understanding, since such a high-resolution image requires much time and memory to process, the image is usually reduced to a lower-resolution image thathas ten thousands of pixels. It may throw away signiﬁcant information included in the high-resolution image. The feasible practical solution is to exploit multiresolution analysis for high-resolution images, where weuse a low-resolution image and recognize rough structure of scene at ﬁrst, and use only the needed parts ofa higher-resolution image later.
Multi-resolution analysis was originated as works
of image processing such as edge detection and region
segmentation in 1980s, and later it was applied to image recognition systems. In works by Z.Li [1] and by
C.L.Tan [2], an image pyramid was introduced by reducing resolution of a high-resolution input image inseveral steps. Then, ﬁrst, the rough structures wereextracted from low-resolution image, and next signiﬁcant parts of higher-resolution images were selected andprocessed based on the rough structure. However, thesystems they implemented are very restricted, becauseof lower ability of computers of those days. Nowadays
since computers have made rapid progress, we can reManuscript received February 20, 2001.
Manuscript revised June 20, 2001.
†
The author is with the Department of Computer Science, The University of Electro-Communications, Chofushi, 182–8585 Japan.
††
The author is with the Graduate School of Information
Sciences, Tohoku University, Sendai-shi, 980–8579 Japan.

alize more large-scale and complicated system.
Here, we introduce multi-resolution analysis to themulti-agent-based image understanding system [3], [4]for eﬀective use of high-resolution images. Our multiagent-based architecture for an image understanding,
MORE (Multi-agent architecture for Object REcognition), is suitable for large-scale and complex recognition
system due to its ﬂexible and extensible architecture.
Almost conventional object recognition systems
with multi-resolution analysis aimed at recognizing asingle object. On the other hand, the objective of oursystem is to recognize multiple objects in a single imageof real-world scene including complex occlusions. In our
research, the “recognition” means to obtain a categoryname of the object, such as “desk” and “chair,” froman image of real-world scene.
Most of the existing object recognition systems with multi-agent architecture, for example, theSchema System [5] and SIMGA [6], didn’t adopt multiresolution analysis. Therefore, target images of thosesystems were restricted to relatively small size images,
and they could not zoom in on signiﬁcant parts of atarget image.
In this paper, we describe design and implementation of a multi-agent-based system employing multiresolution analysis. By resolution-selection mechanism,
the system recognizes objects that couldn’t be recognized in a low-resolution image without much extra
time and memory. We implemented an experimental
system for indoor images on PC cluster system. We
also describe experimental results on the system.
2.

Introducing Multi-Resolution Analysis

Generally in image recognition systems unless morethan a certain quantity of image features such as line
and regions are detected in the initial stage, it is impossible to generate object candidates. Then, our andalmost other image understanding systems have mechanism of “re-recognition.” It means that the systemtries again to recognize objects that were not found in
the initial stage. However, there have not been established methods of “re-recognition.” Many systems onlychange parameters or thresholds in low-level image processing algorithms.

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Table 1

The number of pixels of each region in each level.

region no.
0
1
2

level 0level 1level 2

1,228,800
616,224
54,528307,200
154,056
13,63276,80038,514
3,408

We call this resolution level selected for a certain region “proper level” for the region. Proper level lp is
deﬁned as follows:
lp = arg max{Sil |Sil < th}lFig. 1

An image pyramid for multi-resolution analysis.

In case of on-line image capturing, the system canzoom in on needed parts of scene dynamically by methods of active vision [7]. However, it is impossible forthe oﬀ-line recognition system whose targets is a single
still image. Then, we prepare a high-resolution image
in advance, at ﬁrst use the reduced image, and next
use some parts of the high-resolution image according
to necessity.
Of course, there is an idea that we exploit a whole
high-resolution image in the initial recognition stage.
Usually, computational costs of image processing algorithms for an image recognition system are higher
than O(n2 ), where n is the number of pixels in a target image. Therefore, to reduce total data volume bymulti-resolution analysis is essential for exploiting highresolution images.
In our system, we use a lower-resolution image in
the initial stage, and in the re-recognition stage we select proper resolution from the image pyramid and process only needed part of an image. An image pyramid
is constructed by reducing resolution of an image inseveral steps by a constant magniﬁcation (usually from
0.5 to 0.75) (Fig. 1). We call images in the pyramid an
image of level 0, 1, 2, . . . , respectively, in order fromthe original image.
In the re-recognition stage, the system estimatesregions where undetected object candidates are expected to exist using already detected candidates and“relational knowledge.” It selects resolution of an image in the range where the number of pixels of theregion does not exceed a certain threshold value. In
short, if a region of interest is determined, resolution
level of the image we use for recognition is determined.

(1)

where Sil is the number of pixels of region i in the levell image, and th is a threshold value.
In Fig. 1, the image pyramid consists of three images the resolutions of which are 1280 × 960, 640 × 480
and 320 × 240, respectively. At ﬁrst, the system analyzes the whole image of the highest level, that is, level2 (region no.0), and extracts a ﬂoor, a desk and a display on the desk. Next, it analyzes the region of thedesk candidate extracted from level 0 and its peripheral
region in the image of level 1 (region no.1) to examine if
there are some objects on the desk. If the region wherean object is expected to exist is detected, it analyzes theregion using the image of level 0 (region no.2). Then,a keyboard in front of the display can be detected. Table 1 shows the number of pixels of each region in each
image level in Fig. 1. In case that threshold th is set as200,000, proper levels of region no.0, no.1, and no.2 areselected as level 2, level 1 and level 0, respectively. Thisis because maximum pixel numbers for each region lessthan this th are selected as shown in Table 1, where theselected numbers are shown in boldface.
3.

Recognition Strategy

Targets of our system are scene that consists of artifacts, for example, laboratory scene and PC room
scene. The system recognizes each single object andits relations to other objects in an image.
3.1 Recognition of Each Single Object
To recognize each single object, we prepare prototype
models that represent essential functional structurecommon within the same kind of objects [8]. For example, the functional structure of a “chair” is a combination of a sitting surface and one or four legs, and thatof a “desk” is a combination of a desk face and four legs.
The prototype model is represented by some model elements and a model graph. Model elements are polygons
and straight line segments corresponding to appearanceof parts of an object (Fig. 2 (a)). They have information
about their real shape and their generally expected posein the real world. A model graph represents connection relations between the model elements (Fig. 2 (b)).
Each model has the extent of relative size among each

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Fig. 3Fig. 2Model representation of “desk.”

elements and information which elements are “supportable” and “to-be-supported” (Fig. 2 (c)). Here, the“supportable” element and the “to-be-supported” element mean that the element can support other objects
and the element must be supported by another object,
respectively. These properties are used in the stageof “checking supporting relation” described later. For
example, the model of “desk” has one parallelogramas its supportable element and four vertical line segments whose bottoms are its to-be-supported elements
as shown in Figs. 2 (d), (e), (f).
For estimating regions of an object candidate, ﬁrst,
we extract line segments and regions by conventional
methods, for example, Canny edge detector [9], Houghtransformation, region growing segmentation method,snake [10] and so on. Next, we search a group of line
segments and regions corresponding to each element ofa model. We ﬁt the model to the group of line segments
and regions extracted from an image (Fig. 3). Here, wecall the lines and regions used in model ﬁtting as “basisedges and regions,” and the regions where an object
is estimated to exist as a “candidate region.” A candidate region is the total region expected without occlusions. In addition, by using information about “supportable” and “to-be-supported” elements, we estimate“supportable regions” and “to-be-supported regions” in an image.
We compute conﬁdence value of a candidate Vim
as a weighted sum of the ratio of basis regions to candidate regions of each element, taking the image level
into account:

k 
n

bi
Vim = min 
Wi
, 1
(2)
e
i
i=1
k = 1 + l/(lmax + 1)

(3)

where n is the number of elements, bi is the number ofpixels of a basis region or edge, ei is the number of pixels
of a candidate region or edge, l is the level at which thecandidate is recognized, and lmax is the maximum levelof the image pyramid. Wi (i = 1 · · · n) are weighting
factors that represent the degrees of signiﬁcance of each

Estimating a “desk” candidate.

element in each model. Their valves are given as a priori
informationin each model by hand so that they satisfy
n
W
=
1.0
within each model.
ii=0
For example, since “desk” has ﬁve elements, desk
face and four legs, n is 5, and we set 0.6 for one of
Wi and 0.1 for the rest of them, respectively. If thecandidate was generated at the levels other than 0, Vim
becomes k-th power of the weighted sum of the ratio. k
is calculated as Eq. (3) and takes the value from 1 to 2.
Since Vim is less than 1.0, Vim becomes the smaller, thehigher the level at which the candidate was generated.
Vim is used for resolving conﬂict among candidates.
3.2 Checking of Supporting Relation
All objects except background such objects as ﬂoor,
wall, road, and sky, must be supported by other objects
in the real world due to gravity of the earth. We knowsuch physical law empirically, so we can expect existence of a desk under a workstation in the complex
scene even if the desk can’t be seen. Then, according
to this fundamental rule of the real-world scene, thesystem checks relations which object supports which
object. We call such the relation the “supporting relation.”
Every time the system generates a new object candidate, it examines if the “supporting relation” holds
between already generated candidate and the new one.
By checking supporting relation between objects, thesystem eliminates object candidates that are impossible to exist and estimates actual objects from parts seenin an image.
The “supporting relation” holds when an object
can be considered to locate on another object and tobe supported by it. Checking the “supporting relation”
is carried out by examining whether to-be-supported
regions of an object is almost included in supportableregions of another object. If so, the former object is regarded to be supported by the latter one. We present an
example including a relation that “desk is supported
by floor” in Fig. 4.
In case that two or more candidates can supports
the new candidates, for example, a book is on the ﬂoor
and at the same time the book is on the desk, we regards that the new candidate is supported by the upperYANAI and DEGUCHI: A MULTI-RESOLUTION IMAGE UNDERSTANDING SYSTEM

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resented by combination of “relation name,” “source
object’s name” and “destination object’s name.” Forexample, “on(book,desk)” means “a book is usuallyon a desk.” At present, we have three types of relations, “on,” “in front of” and “on same plane.” Thesystem judges whether each relation is holding using information on supporting relations and relative locationof objects in an image.
Conﬁdence value of relation Vre is determined witha weighted sum of holding relations:


r

Ci ni
(4)
Vre = 1 − exp −hi=1Fig. 4Checking “supporting relation.”Fig. 5 Estimating a “desk” candidate that supports the “workstation” candidate.

candidate, that is, a book is on the desk.
If a candidate has no supporting relation, to-besupported regions of the candidate are regarded as“virtual basis regions” and the system searches a
new candidate with supportable regions including thevirtual basis regions (Fig. 3). In short, “virtual basis
regions” are regions that can be regarded as regions ofsupportable elements of a new candidate. Then, thesystem can detect a new candidate that couldn’t be detected before. For example, when the system generates
a “workstation (WS)” candidate with no supporting relation, it regards the to-be-supported region of “WS” asthe virtual basis regions of a desk face element (Fig. 5).
By this mechanism, the system recognizes an object
occluded by another object.
If candidates except background objects have nosupporting relation, ﬁnally, the candidates are canceled.
3.3 Relational Knowledge
The system has “relational knowledge.” They are descriptions about relative relation generally expected between two objects. It is used for computing conﬁdence
value of relation and expecting the region where own
target object exists with a high possibility. It is rep-

where r is the total number of relations, ni represents
if relation i is holding, that is a value of 0 or 1, andh is a constant. We set 0.4 to h for the experiments.
Ci is a weighting factor that represents the degree ofsigniﬁcance of each relation. At present, it is set 1.0 for
the relation “on” and 0.5 for two other relations.
Equation (4) includes exponential term so that theincrease of Vre is much larger in case where the numberof holding relations increases from one to two, compared to the case where it increases from ﬁve to six.
Conﬁdence value of relation Vre takes the value from0 to 1. It is estimated by checking relational knowledge one by one. It can be regarded as the degree ofnaturalness of existence of a candidate in the scene.
3.4 Conﬂict Resolution
If two or more agents generate diﬀerent candidates inthe same region of an image, conﬂict occurs. Conﬂicting candidates are compared by conﬁdence value V . V
is calculated as a weighted summation of conﬁdence
value of the candidate Vim and conﬁdence value of relation Vre as follows:V = (Vim × S  + Vre × w)/(S  + w)
S  = min(S, 2w)

(5)
(6)

where S is the number of pixels of candidate elements
and w is a constant that controls balance between Vim
and Vre . The weights are decided with taking the sizeof a candidate into account. If the number of pixel
S of the candidate is more than 2w, the ratio of theweight of Vim and Vre is 2 : 1. Otherwise, it is S : w.
This emphasizes Vre for the candidates having the small
number of pixels in the evaluation of the V . We set 2500
to w for the experiments.
As a result of comparison of V , the candidate withthe highest value remains, and all other candidates arecanceled. If the ratio of conﬂicting regions to basis
regions and edges of the canceled candidates is less than0.5, “candidate modiﬁcation re-recognition” is carriedout. In this re-recognition, the conﬂicting candidate is
modiﬁed into a new candidate including no conﬂicting
regions.

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In the calculation of conﬁdence value, some parameters appear. Even if these parameters are changedslightly, the result of conﬂict resolution does not change
in case where one is correct and another is incorrect apparently in the image. In such case, the diﬀerence of
their conﬁdence values is large, so that slight changeof parameters does not aﬀect results. In case where
the diﬀerence of conﬁdence values is small, the resultof conﬂict resolution may change. In that case, however, it is hard to distinguish which one is correct in
the image.
There are no established conﬁdence values ofobject candidates in an image understanding system,
and various evaluation methods have been used in manysystems. We modiﬁed the method used in [11] and applied it for our system.
4.

Overview of the System

4.1 System Architecture
We designed the system based on “MORE” architecture
we proposed in [3]. It is multi-agent-based architecture
and constructed as an assembly of agents that recognize objects from an image in parallel and independently. It enables to recognize various diﬀerent kindsof objects by adding agents. In our system, one agent
consists of a recognition module (RM), a communication module (CM) and candidate objects (CO)(Fig. 6). In addition, the system has a feature extraction module (FE) that extracts image features froman input image. The processing ﬂow among all themodules is message-driven.
RM recognizes only one kind of target objects bysending FE requests to extract image features. CM carries out cooperation among agents. It checks supporting relations to candidates generated by other agentsand resolves conﬂict among the agents. Using supporting relation and relational knowledge every CM has,
it estimates the region where own target object exists
with a high possibility in the re-recognition stage andissues re-recognition requests to RM. RM and CM exist
from the beginning, but CO doesn’t exist before starting of the recognition. It is generated by CM, every
time a new object candidate is found.
Besides agents, the system has FE, which makes animage pyramid and extracts straight edges and regions
by requests from RM of each agent. Since FE plays
an role as a subcontractor of image processing in RMs,
after here we regard FE as being included in RMs anddon’t mention it in this paper.
4.2 Recognition Requests
CM sends recognition requests to RM, and RM starts
recognition processing. There are six kinds of recognition requests (Table 2). One is an initial recognition

Fig. 6 System structure and ﬂow of messages. (a) initialrecognition request (b1) information of an new object candidate (b2) generation of a candidate object (b3) broadcasting
information of a new candidate (c) notifying a new candidateof another agent (d1) objection message (d2) modification
request (d3) information of a modiﬁed candidate (d4) cancellation message (e1) renewal request (e2) information of a renewed candidate (f) supporting request or to-be-supported
request (g) recognition request for vacant regions.

request, and others belong to re-recognition requests.
When each request is issued, in according to the sizeof a region to recognize, CM selects proper resolution
level of the image.
• Initial Recognition Request
This request is issued at the beginning of processing.
Recognition is carried out for a whole input image ofthe highest level.
• Modification Recognition Request
After conﬂict resolution, this request is issued in orderto modify the region of a canceled candidate into a new
region with no conﬂict. For the region of the canceled
candidate in the proper level image, the RM of thesame agent as CO of the canceled candidate tries tore-recognize a new candidate whose basis regions andedges don’t overlap with basis regions and edges of the
opponent candidate in the conﬂict resolution.
• Renewal Recognition Request
In case that a newly generated candidate makes noconﬂict with other candidates, the candidate is rerecognized in its proper level by this request.
• Supporting Recognition Request
If a new candidate except background objects is notsupported by any other candidates, this request is issued in order to search a supportable candidate in theregion under the new candidate in the proper level image. This request is issued in agents whose objects
can support the new candidate according to relational
knowledge.
• To-be-supported Recognition Request
When a new candidate with supportable regions is recognized, this request is issued. The supportable regionsof the candidate are examined by agents whose object
is supported by the candidate according to relational
knowledge.
• Recognition Request for Vacant Regions

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Table 2Six kinds of recognition requests.

request type
initialregion to recognize
whole image

modification

original candidate

resolution levelmax (the lowest resolution)
properrenewaloriginal candidate

propersupporting

the virtual basis region
and its peripheral region
supporting regions andits peripheral region

propera supporting region including
the virtual basis region

properunrecognized regions

propera to-be-supported region being
included in the supporting region of the source candidate ofrequestsnone

to-be-supported

vacant regions

In the end of processing, in case that there are regions
where no candidates detected in an image, this request
is issued as the ﬁnal re-recognition request.
4.3 Flow of Recognition
As was noted before, the processing ﬂow among all
modules is message-driven. We describe the detail ﬂow
of messages and recognition requests in the case shownin Fig. 6.
(a) Initial Recognition
At ﬁrst, CM sends RM an initial recognition request, and RM initiates initial recognition for thewhole region of the highest level image with minimum
resolution (Fig. 6 (a)). If no candidate is detected, RM
executes initial recognition for next lower level image
again.
(b) Generating Object Candidates
When a new candidate is detected at RM, its information is sent to CM (b1). After checking supporting
relation, CM generates CO (b2), and CO broadcasts
information about it for CMs of other agents (b3).
(c) Receiving Information of Candidates
If another CM receiving information of a new candidate founds conﬂict, the CM informs occurrence of the
conﬂict to CO concerned with conﬂict with it (c).
(d) Conflict Resolution
CO send an objection message to CO of the otheragent (d1), and COs concerned with the conﬂict carryout conﬂict resolution by comparing each conﬁdence
value of a candidate and relation. The winning CO remains, and the losing CO sends CM a modification
request (d2), (d3). By the modification request RM
re-recognizes the region of the losing candidate in theproper level image to modify its own region lest conﬂict
occurs. If modiﬁcation fails, the candidate is canceled,
the CO broadcasts a cancellation message (d4) and it
is terminated ﬁnally.
(e) Renewal of Object Candidates
If a new generated candidate doesn’t make conﬂict, aconditionnone

kinds of target objects
all kinds

not including basis regions and
edges
none

same to original candidate
same to original candidate
objects described in relational knowledge
objects described in relational knowledge
allrenewal request is sent from CO to CM, and RM
re-recognizes the region of the candidate in the properlevel image (e1), (e2).
(f ) Estimation of Candidate Region based on
Supporting Relation
If CM receives information of a new candidate without supporting relation with any other candidates, CM
sends RM a supporting request and RM searches a
supportable candidate object for the unsupported candidate in the region under the candidate in the properlevel image (f).
If CM has relational knowledge related to a new
candidate sent from other CM, CM sends RM a tobe-supported request and RM examine supportableregions of the new candidate object in order to detect
its own new candidate.
(g) Re-recognition for Vacant Region
If all modules of all the agents are in the state of waiting
for a message and there is no message on communication lines, the system enters the ﬁnal recognition stage.
In the ﬁnal recognition, if there are regions where no
candidates detected in an image, a recognition request for vacant regions is sent (g). Re-recognition
for these regions is carried out.
After the ﬁnal recognition, the whole recognitionof the system completes.
5.

Recognition Results

We have implemented an experimental system for indoor images with six agents (“desk,” “chair,” “ﬂoor,”“book,” “pen,” and “work station (WS)”) on PC cluster system that consists of six PCs (Intel Celeron
450 MHz, memory 128 MB) using the PVM library [12].
In this system, each agent is implemented on each onePC.
5.1 Examples of Recognition Results
A sample indoor image (1280 × 960) in Fig. 7 includes a

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Table 3resolution
320 × 240
640 × 480
1280 × 960
multi-resolution

Fig. 7 An indoor
sample image.

Fig. 9

Execution time.
time[sec.]
8.3
38.3
× (out of memory error)
19.6

Fig. 8 Recognition result using multi-resolution
images.

The region of the desktop.

Fig. 11 A complex indoor sample image.

Fig. 12 Recognition result for Fig. 11.

Fig. 13 Another complex indoor sample image.

Fig. 14 Recognition result for Fig. 13.

Fig. 10 The region of the pencandidate.

“book,” a “pen” and a “WS” on a “desk.” In the experiment by single-resolution recognition for the 320 × 240
image, only a “desk,” “ﬂoor” and a “WS” were recognized correctly, but a “pen” and a “book” were notrecognized.
In multi-resolution analysis, we used an image
pyramid consisting ﬁve level images from level 0 to
4. Reduction ratio was 0.7, and size of the highestlevel image (level 4) was 308 × 231. In the experiment, at ﬁrst, in initial recognition for level-4 image a“desk,” “ﬂoor” and a “WS” candidates were generated.
Next, by relational knowledge of on(book,desk) andon(pen,desk), “book” agent and “pen” agent initiatedre-recognition for the region of “desk” and its peripheral
region in level-3 image by a to-be-supported request, andgenerated a “book” and “pen” candidates, respectively(Fig. 9). After that, both candidates were re-recognizedin level-0 image by a renewal request (Fig. 10). Finally,
we obtained a result shown in Fig. 8. While this process, conﬂict between a keyboard part of a WS and abook occurred, and some parts of a bookshelf of left
hand were recognized as a book and a pen candidate.
However, they were canceled ﬁnally by conﬂict resolution and checking “supporting relation.”
We show execution times in case of singleresolution for three sizes of the image and multiresolution for the sample image (Fig. 7) in Table 3.
With 640 × 480 image all objects were detected, but
the execution time was about ﬁve times as long as one
with 320 × 240 image. The system employing multiresolution analysis could recognize all objects, and its
execution time was only about twice as long as one with
320 × 240 image.
We show more complex indoor scene image in

Fig. 11 and its recognition result in Fig. 12. In the experiment, only with 320×240 image the system couldn’t
recognize four WSs on the back desks. However, it recognized such a complex image by forming ﬁve level resolution images.
Another sample image in Fig. 13 is also relatively
complex. Its recognition result is shown in Fig. 14. In
this experiment, one back WS was not recognized, butother ﬁve WSs could be recognized correctly. Especially, although three recognized WSs on the back wereappeared as only small regions in the image, they could
be recognized by multi-resolution analysis.
5.2 Experimental Results for 20 Images
We made experiments for 20 images including various
indoor images from a simple image like Fig. 7 to a relatively complex image like Fig. 11 and Fig. 13. Figure 15
shows 12 images out of 20 samples images used in thisexperiments, and Fig. 16 shows recognition results forthese 12 images in the multi-resolution recognition.
We divided the results into “almost correct,” “halfcorrect” and “almost incorrect” in terms of the recognition rates. The recognition rates for “almost correct,”“half correct” and “almost incorrect” are 80%–100%,30%–80% and 0%–30%, respectively. Table 4 shows theexperimental results for 20 images in case of both the

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1649

For future work, we plan to construct recognition
modules by machine learning methods and introduce
more eﬀective cooperation mechanism to improve ability of individual recognition modules.
References

Fig. 15 12 out of 20 images for experiments. Images in theupper row are quite simple, ones in the lower row are complex,
and ones in the middle row have middle complexity.

Fig. 16

Recognition results for above 12 images.

Table 4

Results for 20 images.

single-resolution (320 × 240)
multi-resolution (5 level)almostcorrect
5
12

half
correct
83almostincorrect
7
5

single-resolution recognition and the multi-resolution
recognition.
In the single-resolution recognition, only ﬁve images were interpreted correctly, but in the multiresolution recognition, 12 images were interpreted correctly. Both numbers of almost incorrect results werecomparable. This is because these images were toocomplex to extract signiﬁcant image features in the initial recognition stage, so that eﬀective re-recognition
couldn’t be initiated.
6.

Conclusion

In this paper, we applied multi-resolution analysis to a
multi-agent-based image recognition system. We proposed and realized the system that can use a highresolution image eﬀectively without much extra processing time.

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Keiji Yanai
received B.Eng. and
M.Eng. degrees from University of Tokyoin 1995 and 1997, respectively. From
1997, he is a research associate of Department of Computer Science, the University of Electro-Communications. His recent research interests include an image
understanding system and a multimedia
database.

IEICE TRANS. INF. & SYST., VOL.E84–D, NO.12 DECEMBER 2001

1650

Koichiro Deguchi
received B.Eng.,
M.Eng. and Dr.Eng. degrees from University of Tokyo in 1974, 1976 and 1980, respectively. From 1984 to 1987, he wasan associate professor of Department ofInformation Engineering, Yamagata University, and from 1988 to 1998, he wasan associate professor of Department ofMathematical Engineering and Information Physics, University of Tokyo. From
1998, he is a professor of Graduate Schoolof Information Sciences, Tohoku University. His recent researchinterests include computer vision, signal processing and multi
processor architecture for them.