INDEXATION OF WEB PAGES BASED ON THEIR VISUAL RENDERING

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1 INDEXATION OF WEB PAGES BASED ON THEIR VISUAL RENDERING Emmanuel Buno Univesité du Sud Toulon-Va / LSIS CNRS BP 20132, F La Gade buno@univ-tln.f Nicolas Faessel LSIS CNRS Domaine Univesitaie de Saint-Jéôme F Maseille Cedex 20 nicolas.faessel@lsis.og Jacques Le Maite Univesité du Sud Toulon-Va / LSIS CNRS BP 20132, F La Gade lemaite@univ-tln.f ABSTRACT This pape pesent the indexation method that we ae actually developing fo infomation etieval in web pages based on thei visual endeing. This method can be summaized as follows. Fistly, the visual endeing of a page poduced by a web bowse is segmented into ectangula blocks consideed as unifom in elation to some visual citeia. Secondly each block is indexed accoding to the vecto space model. Finally, the indexation of each block is enhanced by its visual significance and by the indexation of the blocks which ae associated to it by inclusion, neighbohood, o efeence elations. KEYWORDS Infomation Retieval, Indexation, Vecto Space Model, Web Page Segmentation 1. INTRODUCTION Ealy infomation etieval systems (IRS) wee esticted to textual documents. Late, when the size of compute memoies has become enough to stoe images, content based image etieval systems have been developed and at the pesent time content based etieval systems fo othe digital data (video, speech ) ae available. One of the challenges of infomation etieval is now to develop high pefomance etieval systems fo web documents taking into account thei stuctues and meging the infomation povided by each kind of media (text, image, video, speech ) they contain. To be efficient this etieval must not only use the logical stuctue of the documents (thei DOM tee, fo example) but thei visual endeing too, fo the following easons: 1. The logical stuctue of a document is not a good epesentation of its visual endeing. Fo example, the same visual endeing can be geneated fom vey diffeent DOM tees, as shown by Figue The location of a component and its visual appeaance can povide useful infomation about the significance of this component. 3. The infomation contained in a given component can be useful fo the undestanding of the content of components close to it. Fo example, the indexation of an image can be enhanced by the indexation of its caption.

2 <html> <head> <style type='text/css'> td {backgound: lightgay; table {width:100%;height:100%; text-align: cente; bode-spacing:10px;} #menu {width: 30%;} #title {height:10%;} </style> </head> <body> <table> <t><td id='title' colspan='2'>title</td></t> <t><td id='menu'>menu</td> <td id='main'>main Pat</td> </t> </table> </body> </html> (a) HTML layout with tables <html> <head> <style type='text/css'> body {magin:0;padding:0;} div {magin:1%; backgound:lightgay;} #title {position:absolute; width:98%;height:10%;} #menu {width:30%;height:85%; position:absolute; top:11%;left:0;} #main {width:67%;position:absolute; top:11%;left:31%;height:85%;} </style> </head> <body> <div id='title'>title</div> <div id='menu'>menu</div> <div id='main'>main Pat</div> </body> </html> (b) HTML layout with divisions (c) The visual endeing in a web bowse Figue 1. Two diffeent HTML desciptions of a page, fo the same visual endeing 4. The answe to a quey is moe pecise than if the pages wee globally indexed. Only elevant page s components ae etuned instead of the whole page. That doesn t pevent the use to ask fo a genealization of this answe to the including blocks. It is this appoach that we have begun to exploe in a way that we pesent in this pape. Fo the time being, we only conside web documents educed to a unique page. The method we ae expeimenting can be summaized as follows: 1. The visual endeing of a page is segmented into ectangula blocks consideed as unifom in elation to some citeia. This can be done by accessing the CSS box model of this page povided by mozilla s ende engine (called Gecko). 2. Each block is indexed accoding to the vecto space model. 3. The indexation of each block is enhanced by its visual significance and by the indexation of the blocks which ae associated to it by inclusion, neighbohood, o efeence elations. The emainde of this pape is oganized as follows, section 2 pesents the page segmentation algoithm that we have chosen, section 3 is devoted to the indexation of a web page, and section 4 concludes.

3 Page 1 1 DOM_tee 1 Node Block location significance (α) own own vecto ( z ) global vecto (z ) 1 Thésauus Tem type contibution (β) Figue 2. Modeling of the content of a web page 2. PAGE SEGMENTATION Seveal algoithms have been poposed to segment a web page into blocks consideed as unifom in elation to some visual citeia. We only cite hee those we have studied fo ou poject. VIPS (VIsion-based Page Segmentation) is a top-down algoithm poposed by D. Cai, S.Yu, J. R. Wen and W. Y. Ma. (Cai, 2003) to detect the stuctue of a web page based on its visual epesentation. Zou et al. (Zou, 2006) have poposed a simple vesion of VIPS using the ecusive X-Y cut algoithm fo on-line medical jounal papes analysis. The ecusive X-Y cut algoithm was initially elaboated by G. Nagy and S. Seth (Nagy, 1992) in the famewok of a system fo technical jounal analysis. K. Simon and G. Lausen (Simon, 2005) have developed a tool named ViPER (Visual Peception-based Extaction of Recods) to extact epetitive infomation contents with espect to the use s visual peception of a web page. Togethe with H. Boley they use it to maps HTML documents to tables (Simon, 2006). B. Küpl, M. Hezog, and W. Gattebaue, have implemented two algoithms fo table detection in a web page using its endeing poduced by the Mozilla bowse. The fist (Küpl, 2005) opeates top-down using a vaiant of the X-Y cut algoithm and the second (Küpl, 2006) opeates bottom-up by gouping wod bounding boxes and applying a set of heuistics fo detecting columns and table. Because of its elative simplicity, we decided to use the algoithm poposed in (Zou, 2006) to segment a web page into a set of blocks. This algoithm is caied out in two stages. The fist stage consists in tansfoming a DOM tee issued fom a web bowse into a zone tee in which, a zone node is a visual gouping of DOM nodes. This tansfomation is ealized in accodance to the following steps: 1. The HTML page is displayed by a web bowse which poduces a DOM tee. 2. The DOM tee is tavesed in post ode to etieve the spatial positions of each node and to classify them eithe as insignificant nodes (nodes which ae not visualized), in-line nodes (nodes that do not intoduce line beaks) and line-beak nodes (nodes which ae neithe insignificant no in-line). 3. A oot zone is ceated which coesponds to the <BODY> node of the DOM tee. An empty divisible zone list is ceated which is initialized with this oot zone. 4. A divisible zone z is chosen among the list of divisible zones. If z has ovelapping childen zones, they ae sepaated and each one is in tun added to the divisible zone list. If z has no ovelapping

4 childen zones, the leaf zones (in-line nodes and deepest line-beak nodes) of z ae collected and the ecusive X-Y cut algoithm is applied to mege leaf zones which ae geometically close. 5. Step 4 is epeated until the list of divisible zone is empty. The second stage consists in poducing the blocks fom the zone tee. A block is a set of zones which visually ae consideed as a whole. Fistly, citeia ae chosen fo sepaating two adjacent zones which can be, fo example, a gap lage than a given theshold o a diffeence in visual appeaance. Once these citeia ae chosen, the zone tee is tavesed in peode and the blocks ae selected. This algoithm is only intended fo the extaction of textual blocks and uses vey thee citeia fo the sepaation of these blocks: the gap between the blocks, the backgound colo and the font attibutes. We will extend it in ode to extact blocks containing non textual data (image, videos ) and to take into account a lage set of citeia fo sepaating blocks. 3. INDEXATION The infomation etieval model we popose is based on the epesentation of a web page schematized in Figue 2. The visual endeing of a page is decomposed into blocks which ae poduced by the segmentation pocess explained in section 2, which mainly consists in egouping nodes of the DOM tee of this page. A block is identified by its location in the page (coodinates of the left lowe and the ight uppe cones). Each block of a page is indexed. Indexation of a block b combines: the infomation contained in b descibed by a set of weighted tems which we suppose to be poduced by an indexing tool adapted to the kind of media contained in b (a block is mono-media), the significance of b which depends, among othes, on the location of b in the page (at the cente, at the top, at the bottom) and on its visual appeaance (font size, colo backgound ), the infomation povided by the blocks which ae elated to b (efe b, ae included in b, ae close to b ), The indexation of a block is done accoding to the vecto space model (Baeza-Yates, 1999, chapte 2). We popose to associate two vectos to a block: its own vecto which descibes the infomation contained in this block and its global vecto which is an enhancement of its own vecto by the visual significance of the block and the infomation povided by the blocks which ae elated to it. The global vecto of a block of a page is a linea combination of the own vecto of this block and of the own vectos of the blocks of the same page elated to it. Let: P be the set of pages to be indexed, T be the set of tems (the thesauus) indexing the blocks of P, m be the cadinality of T, p be a page of P, n p be the numbe of blocks in p, b p,i be the ith (1 i n p ) block of p, own b i be the m-dimensional vecto epesenting the own indexation of b i, α p,i (a numbe 0) be the significance of b p,i in p, β p,k,i (a numbe 0) be the contibution of block b p,k to the indexation of b p,i with β p,i,,i = 0, the global vecto descibing the content of the block b p,i of a page p of P is calculated by the following fomula: b p, i = α + β own own b p, i p, ibp, i p, k, ibp, k k = 1, n The β paamete is paticulaly inteesting fo the indexation of not textual blocks because extacting, with a easonable degee of pecision, the tems descibing a non textual data like an image o a video necessitates having a minimum of knowledge of the semantics of this data. This semantics can be povided by the text efeing this data: its caption, fo example. Let us conside, fo example, two blocks b 1 and b 2 of a given page such that b 1 contains an image i and b 2 contains the caption of this image located immediately below b 1. To enhance the indexation of the image i by the own indexation of its caption, it suffices to set β 21

5 to 1. In the case whee no tool is available to index the image, its indexation will be educed to the one of its caption. The β paamete is also inteesting to index a block fom the indexation of its constituent blocks. Let us conside, fo example, a block b 1 of a given page including two blocks b 2 and b 3. To enhance the indexation of b 1 by the own indexations of b 2 and b 3 it suffices to set β 21 and β 31 to 1. We ae actually studying diffeent stategies fo the evaluation of the α and β paametes. We will use typologies of web pages (Leveing, 2006) and machine leaning techniques fo discoveing ules allowing quantifying the significance of a block o of a elation between blocks. Fo the β paamete, we will take advantage of woks those done by ou team since a few yeas on image etieval by meging textual and visual infomation (Tollai, 2005)(Tollai, 2006) o by K. Banad and M. Johnson on the disambiguation of the text descibing an image by the visual content of this image (Banad, 2005). 4. CONCLUSION In this pape we have pesented the model that we ae actually developing fo infomation etieval in web pages based on thei visual endeing. In this model, the visual endeing of a page is decomposed in a set of blocks which ae extacted fom the logical stuctue of the page (its DOM tee) by means of the segmentation algoithm poposed in (Zou, 2006). Indexation is done accoding to the vecto space model. The content of each block of a page is epesented by a vecto which combines the infomation contained in this block descibed by a set of weighted tems, its visual significance and the infomation povided by the blocks which efe it. We have poposed a fomula to calculate this vecto which depends on two paametes: the α paamete which quantify the weight of the visual significance of a block and the β paamete which quantify the contibution of a block to the indexation of anothe block. We ae now woking on the leaning of the α and β paametes and on the evaluation of this model. REFERENCES Baeza-Yates, R. and Ribeio-Neto, B Moden Infomation Retieval. Addison-Wesley. Banad, K., Johnson, M., Wod Sense Disambiguation with Pictues. In Atificial Intelligence, Vol. 167, No 1-2, pp Cai, D. et al., Extacting Content Stuctue fo Web Pages Based on Visual Repesentation, Poceedings of the 5th Asia Pacific Web Confeence, Xian, China, Küpl, B. et al., 2005, Using visual cues fo extaction of tabula data fom abitay HTML documents. Poceedings of the 14th Intenational Confeence on Wold Wide Web (WWW 2005), Chiba, Japan, Küpl, B. and Hezog, M., 2006, Visually guided bottom-up table detection and segmentation in web documents. Poceedings of the 15th Intenational Confeence on Wold Wide Web (WWW 2006), Edingbugh, Scotland, Leveing, R., Cutle, M., The potait of a common HTML web page. Poceedings of the ACM Symposium on Document Engineeing (DocEng 2006), Amstedam, The Nethelands, Nagy, G. et al., 1992, A Pototype Document Image Analysis System fo Technical Jounals. In Compute, Vol. 25, No 7, pp Simon, K. and Lausen, G., 2005, ViPER: Augmenting Automatic Infomation Extaction with Visual Peceptions. Poceedings of the 2005 ACM CIKM Intenational Confeence on Infomation and Knowledge Management (CIKM 2005), Bemen, Gemany, Simon, K. et al., 2006, Fom HTML Documents to Web Tables and Rules. Poceedings of the 8th Intenational Confeence on Electonic Commece (ICEC 2006), Fedeicton, Canada, Tollai, S. et al., 2005, Enhancement of Textual Images Classification using Segmented Visual Contents fo Image Seach Engine, In Multimedia Tools and Applications, Vol. 25, n 3, pp Tollai, S., Indexation et echeche d'images pa fusion d'infomations textuelles et visuelles, PhD Thesis, Univesité du Sud Toulon-Va, Fance. Zou, J. et al., 2006, Combining DOM Tee and Geometic Layout Analysis fo Online Medical Jounal Aticle. Poceedings of the ACM/IEEE Joint Confeence on Digital Libaies (JCDL 20006), Chapel Hill, Noth Caolina, USA,

An Unsupervised Segmentation Framework For Texture Image Queries

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