3D Model Retrieval Method Based on Sample Prediction

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1 20 Iteratioal Coferece o Computer Commuicatio ad Maagemet Proc.of CSIT vol.5 (20) (20) IACSIT Press, Sigapore 3D Model Retrieval Method Based o Sample Predictio Qigche Zhag, Ya Tag* School of Computer & Iformatio Sciece, Southwest Uiversity Chogqig, Chia zhqigche@gmail.com Abstract. This paper presets a ovel method for 3D model retrieval combiig the cotet-based retrieval ad the text-based retrieval. Algorithm merges the cotet-based retrieval ad the text-based retrieval through the sample library. The user oly eed to eter a keyword ad the algorithm will search the 3D models with a keyword i the database. It will automatically select the sample for the user to retrieve the 3D models without ay keyword. Experimetal results demostrated the efficiecy ad performace of the proposal algorithm. Keyword: sample library, 3D model retrieval, cotet-based retrieval. Itroductio With the developmet of the 3D modelig methods ad graphics hardware techology, the umber of 3D model is icreasig rapidly. The key questio of 3D model retrieval is how to fid required models quickly, completely ad accurately. The origial three-dimesioal model retrieval method is based o the text retrieval of three-dimesioal model. This search method usually eeds oe or more keywords describig cotets of a 3D model ad the we ca get the required models through the keyword search techology, which is a simple ad easy method to use. However, the characteristics of a 3D model are rich i cotet ad low i abstractio, which therefore are difficult to be fully described with a few key words i may cases. With the icreasig umber of 3D models, it is almost impossible to make the artificial keywords for each three-dimesioal model. The cotet-based retrieval came ito beig. The 3D mode retrieval based o cotet uses the features of a 3D model icludig the shape, appearace, skeleto ad other basic visual feature. By extractig ad matchig the characteristics of 3D models, we ca idetify the similarity amog them. The biggest advatage of cotet-based retrieval is that the approach has objectivity. However, the curret retrieval system based o cotet requires the user to provide a sample or had-draw sample, which will lead to the icoveiece to the user. Ad whe a sample or had-paited sample the user provides is iaccurate, the search efficiecy will reduce greatly.. I this paper, we itegrated the advatages of the two retrieval methods, combiig the two methods by sample database. 2. The Three-dimesioal Model Search Algorithm based o Sample Database *Correspodig author. ytag@swu.edu.c 675

2 I this sectio, a oel algorithm is proposed to retrieve 3D models from model database. Our approach falls ito the boudary-based category. The followig shows the diagram for this approach. After the user eters the keyword, firstly retrieve models with keywords. Next, a sample is selected from the sample library accordig to the keyword etered by the user ad the models without ay keywords are retrieved based o the cotet. Fially update the sample database accordig to users choice. 2.. The 3D Model Retrieval based o the Text Model files i the database are traversed ad the fileame of every model is take out which is the keyword of the model. Match the query keyword etered by user to the keyword of every model, ad the put the successful model i the result model library Sample Selectio a) Look through all the samples i the sample database ad take out the keywords of each sample. b) Match the query keyword etered by users to the keywords of the sample. c) If there is ay successful matched model, the use the model as a sample for retrieval based o the cotet. If there is o matched model, the algorithm will select the first model from the result model library of 2. as the sample of cotet-based retrieval The Three-dimesioal Model Retrieval based o Cotet This paper presets a 3D retrieval algorithm based o the cetric distace distributio i this experimet. Basic idea of this algorithm is as follows: Samplig vertices: we use halto sequece [] to sample 024 poits i accordace with the priciple of equal area [2]. First, for each triagle, we compute its area: * * * ABC = S S a S b S c ( ) ( ) ( ) S = ( a+ b+ c)/2 a, b, c is the legth of each edge of a triagle, Store the area of each triagle i a cumulative array alog with the order computed, ad the the dimesio of this array is the umber of model triagular facets. Geerate 024 radom umbers rakig from 0 to the total cumulative area accordig to the priciple of halto. Radomly select a iteger m ad choose a iteger t less tha m. ad the the 024 radom umbers are geerated by the followig rules: M is expressed as a polyomial t: m= at 0 676

3 Use the coefficiet series to obtai the radom umber : Order m=m+ ad repeat util the 024radom umber is obtaied. mradom = at + Perform a biary search o the array of cumulative areas util we fid the referece umber of the triagle facets correspodig to the idex umber, so, you ca fid a triagular patch is proportioal to the probability of its area. For each selected triagle with vertices (A, B, C), we costruct a poit o its surface by geeratig two radom umbers, r ad r 2, betwee 0 ad, ad evaluatig the followig equatio: P = r A+ r r B+ rrc ( ) ( ) 2 2 This algorithm is take to esure that the fial series of vertices i accordace with the priciple of equal area are distributed i the model surface evely. Accordig to Priceto Uiversity, we radomly select 024 vertices. Distace calculatio: calculated the distaces from ay poit of 024 samplig poits to the ceter of the model. The ceter of a model is calculated as follows: Pi i = P = P meas the ceter-of-mass coordiate; meas the umber of model vertices. Calculate the distace from each samplig poit to P : = ( ) + ( ) + ( ) P (,, ) d x x y y z z i i i i i i i the cetric coordiate of the model.. Costruct the feature vector of the model as follows: If there is d 0 ( ) x y z is the coordiate of the ith vertex ad = { d } max max i { d } p( x, y, z) is dmi = mi i Divide[ dmi, dmax ] ito some subitervals, ad the iterval of each iterval is d: dmax dmi d = 0 Calculate the percetage accordig to the distaces i each subiterval as the feature vector of the model. 3. The similarity measure: Set the feature vector of ay two models ca be expressed as: X = ( x x x ), Y = ( y y y ), 2,,, 2,, Usig distace [3] of Mikowski to measure the similarity betwee two models: D( X, Y) = x y r r i i r =, 2,, 677

4 3.. Update of Sample Database Sample library is set before retrieval. Two problems will appear i the process of the sample library predictio. Firstly, the forecastig sample is ot the user's expectatio. To solve the problem, the system is completed o the first search, ad we provide the secod search iterface for users, through which user ca select a sample from the result model library to retrieve for the secod accordig their eed. Secodly, the sample database is ot complete; the sample database does ot cotai the samples of all types of 3D model. As for this problem, whe selectig the sample, if there is ot ay sample i the sample library, the proposed algorithm will choose the model i the result model library as the sample for cotet-based retrieval. After the user selects a model from the result model library, our approach will put this model ito the sample library to update ad supplemet the sample library. 4. Experimet Accordig to the proposed algorithm, we use java laguage to develop a 3D model retrieval system i Widows platform. Whe a user eters a keyword for retrieval, the system will retrieve the models that match to the keyword, ad the automatically select the sample for the user to retrieve the similar models to the sample The stadard test database [4 provided by shape aalysis group of Priceto Uiversity, cotaiig 907 models, is used. Calculate separately the recall ratio ad precisio for each part, Table is created. The defiitio formula [5] for searchig the recall ratio r ad checkig precisio ratio s is as follows: r = + Rm s = + Rf Amog them, Rm = beig ot searched ad it is related, = searchig ad it is related, Rf = searched but it is relevat. search key average recall ratio average checkig accuracy rate huma body 73.2% 80.0% house 75.8% 75.0% carriage wheel 84.6% 85.0% flower 7.0% 70.0% Table : The recall ratio ad checkig accuracy rate i experimet The experimetal results show that the combiatio of text-based ad cotet-based retrieval ot oly provides the users with the coveiece, but the retrieval efficiecy also icreases sigificatly. 5. Coclusio This paper presets a ovel algorithm for 3D model retrieval based o the sample predictio. Pre-settig sample i the sample database, the user oly eed to eter a keyword, the algorithm will automatically select the sample from the sample database. Comparig with existig algorithms, the experimetal results show that the recall ratio ad checkig accuracy rate of our algorithm are improved sigificatly. 6. Referece [] Lei Guiyag. About some studies i Mote Carlo ad drafted Mote Carlo Method [D]. Hagzhou: Zhejiag Uiversity,

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