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原型基于颜色的图像检索与MATLAB (2).pdf
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Prototyping Color-based Image Retrieval with MATLAB®
Petteri Kerminen1, Moncef Gabbouj2
1Tampere University of Technology, Pori, Finland
2Tampere University of Technology, Signal Processing
Laboratory, Tampere, Finland
Abstract
Content-based retrieval of (image) databases has become more popular
than before. Algorithm develop-ment for this purpose requires
testing/simulation tools,but there are no suitable commercial tools on the
market.
A simulation environment for retrieving images from database according
histogram similarities is presented in this paper. This environment allows the
use of different color spaces and numbers of bins. The algorithms are
implemented with MATLAB. Each color system has its own m-files.
The phases of the software building process are pre-sented from system
design to graphical user interface (GUI). The functionality is described with
snapshots of GUI.
1. Introduction
Nowadays there are thousands or hundreds of thousands of digital images
in an image database. If the user wants to find a suitable image for his/her
purposes, he/she has to go through the database until the correct image has
been found or use a reference book or some “intelligent” program. Video on
demand (VoD) services also requires an intelligent search system for
end-users. VoD systems’ search methods differ slightly from image
database’s methods.
A reference book is a suitable option, if the images are arranged with a
useful method, for example: 1)categories: animals, flags, etc, 2) names
(requires a good naming technique) or 3) dates. An experienced user can use
these systems as well as textual searches (keywords have to be inserted in a
database) efficiently. There are situations when a multi-language system has
to be used. There a language independent search system’s best properties
can be utilized. A tool which is based on the images’ properties can be made
language independent. These properties can be for example color, shape,
texture, spatial location of shape etc.
In the MuVi-project [1] this kind of tool is under construction. It will cover
the properties presented above.Research work on content-based image
retrieval has been done in [2 – 6]. The system, which is presented in this paper,
is a simulation environment, where MuVi’s color content based retrieval has
been developed and tested.
2. System development
MATLAB is an efficient program for vector and matrix data processing. It
contains ready functions for matrix manipulations and image visualization and
allows a program to have modular structure. Because of these facts MATLAB
has been chosen as prototyping software.
2.1 System design
Before any m-files have been written, the system designhas been done. A
system design for the HSV (hue, saturation and value) color system based
retrieval process is presented in Figure 1. Similar design has been done for all
used color systems.
Figure 1: Function chart for HSV color space with 27 bins histogram.
Tesths27 is the main function for this color system and this number of bins.
It calls other functions(hs27read, dif_hsv and image_pos) when needed. Each
color system has a main function of its own and variable number (2 – 3) of
sub-functions. If there is no need for color space conversion there are 2
functions,otherwise 3 functions on the first branch of the function chart.
The function call of the main function is:
matches=tesths27(imagen,directory,num)
The variable imagen specifies the query image’s name and path. The
directory is a path of the image database and num is a desired number of
retrieved images.
2.2 Functions
At this moment there are functions implemented for four color spaces:
HSV, L*a*b*, RGB and XYZ [7]. Each color space has from 2 to 4
implementations for different numbers of bins. There are altogether 14 main
functions.
For some color systems it is possible to make these functions dynamic, i.e.
dynamic histogram calculation. Every color system / bin combination requires
its own histograms and these can be made only with an exhaustive method
(pixel by pixel). Histogram calculation takes ½ - 5 minutes per image, each
approximately 320×240 pixels, depending on the complexity of the color space
on 150 MHz Pentium. Thus it is not reasonable to let the user select a bin
number freely, especially in the case of large databases.
The functions have been named so that the names contain information of
the color space used, the purpose of the functions and the number of used
bins. Some functions, for example image_pos, have been used by many or all
main functions and these functions have not been named as described above.
The main function checks, if the function call is correct. If the query
image’s name doesn’t contain a path, the function assumes that the image is
situated in the database directory. In addition to this, the main function checks,
if the query image already has a histogram in the currently used database. If
the required histogram is not there, the image read (for example hs27read)
function is called. This function also normalizes pixel values and arranges
image matrix data to a vector format. After that stage a color space conversion
function (if needed) is called. Finally a quantization function builds the
histogram with the correct number of bins.
The histogram will then be saved into the database directory. If the
histogram already exists there, the three previous steps will not be executed.
Now the query image has been analyzed. Then the main function will go
through all images in the database directory with an almost similar algorithm
as in the case of the query image. The difference is that now there will be a
histogram difference calculation between the query image’s and current
image’s histogram. Finally the image_pos function will be used to put a query
image and the desired number of best match images on the display.
2.3 Linking
It is not possible to use a program before the main function and
sub-functions are connected to each other. The main function will be called
from the command line or through the graphical user interface, which will be
presented later in this paper. In both cases the function call will contain the
same arguments. For multi-level search purposes separate main functions
have been implemented, but it is possible to utilize “normal” functions and add
one parameter, where the best matches array can be transferred for second a
stage comparison function.
The main function calls an image read function with the image’s name.
The histogram will be returned to the main function. If a color space
conversion is needed, the conversion function will be called from the read
function with r, g and b –vectors. The histogram will be returned to the calling
function. Finally the histogram build function will be called with converted color
vectors. This function returns a quantized histogram, which will go through all
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