机械专业中英文文献翻译英文原文Prototyping Color-based Image Retrieval with MATLABAbstracContent-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. IntroductionNowadays 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 databases 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 systems best properties can be utilized. A tool which is based on the images properties can be madelanguage 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 MuVis color content based retrieval has been developed and tested.2. System developmentMATLAB 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 designBefore 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. Eachcolor 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 images name and path. The directory is a path of the image database and num is a desired number of retrieved images.2.2 FunctionsAt 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 - 5 minutes per image, eachapproximately 320240 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 images name doesnt 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 s