Efficient Mean Shift Algorithm based Color Images Categorization and Searching

Transcription

1 152 Effcent Mean Shft Algorthm based Color Images Categorzaton and Searchng 1 Dr S K Vay, 2 Sanay Rathore, 3 Abhshek Verma and 4 Hemra Sngh Thakur 1 Professor, Head of Dept Physcs, Govt Geetanal Grl s College, Bhopal (MP) Inda 2 Asst Professor, Head of Dept Physcs, Sr Satya Sa Inst of Sc & Technology, Sehore (MP) Inda 3,4 Dept Computer Scence & Engneerng, Sr Satya Sa Inst of Sc & Technology, Sehore (MP) Inda Abstract Interest n mage retreval has ncreased n large part due to the rapd growth of the World Wde Web The explosve growth of dgtal mage collectons on the Web stes s callng for an effcent and ntellgent method of browsng, searchng, and retrevng mages Content-based mage retreval, a technque whch uses vsual contents to search mages from large scale mage databases accordng to user s nterests In our approach, the color features are extracted usng the mean shft algorthm, a robust clusterng technque, whch does not requre pror knowledge of the number of clusters, and does not constran the shape of the clusters Domnant obects are obtaned by performng regon groupng of segmented thumbnals The category for an mage s generated automatcally by analyzng the mage for the presence of a domnant obect Categores descrbed here are of statstcal and syntactcal descrptons rather than semantcally The mages n the database are clustered based on regon feature smlarty usng Eucldan dstance Placng an mage nto a category can help the user to navgate retreval results more effectvely Extensve expermental results llustrate excellent performance Keywords Content-based, Segmentaton, Category, Indexng 1 INTRODUCTION Wth the advent of mage processng and pattern recognton technques, there has been consderable progress n the area of content-based mage retreval durng the past decade Gven the potentally large collecton of multmeda obects such as mages, vdeo and documents n the Internet, content-based retreval technques have begun to play a maor role n web-based multmeda nformaton search Content-based mage retreval technques extract relevant mages from an mage archve by computng mage smlarty between the query and the stored mages based on mage content Image retreval systems fnd applcatons n medcal mage database, electronc pantng or museum catalogues, and trademark regstraton systems For large mage archve, n order to meet nteractve mage retreval requrement, computatonally effcent technques are needed for mage feature extracton, ndexng, and smlarty rankng Wth the advances n multmeda technologes and the ncreasng emphass on multmeda applcatons, the producton of mage and vdeo nformaton has resulted n large volumes of mages and vdeo clps Ths trend s lkely to contnue, and to cope effectvely wth the exploson of multmeda nformaton, mage-based systems have been proposed to properly organze and manage ths nformaton for rapd retrevals [1] The use of low-level vsual features to retreve relevant Informaton from mage and vdeo databases has drawn much research attenton n recent years Color s perhaps the most domnant and dstngushng vsual feature Tools avalable for searchng for an mage wthn an arbtrary mage collecton, such as n the Internet, s stll far from satsfactory Ths s because the range of mages s wde and the content of the mages s complex Most well known Internet mage searchng tools (eg Google Image Search - use mage flename as the prmary means of ndexng mage attrbutes Ths type of mage ndexng nevtably fals as t s based on the flawed assumpton that mage content s always reflected correctly by the mage flename The remander of the paper s organzed as follows In Secton II, archtecture of the proposed system s dscussed Secton III mage segmentaton of the proposed system In secton IV, category generaton Secton V, query processng Secton VI and VII, concludes the paper and future extenson of the present work 11 Prevous Work A new mage representaton whch uses the concept of localzed coherent regons n color and texture space s presented [2], [3] Images can be retreved by specfyng a combnaton of RGB color values, textural measures, and more recently usng other features such as shapes and spatal relatons between regons n the mage [5] Color s a low level feature, and by tself cannot adequately descrbe obects n mages To enable obects to be extracted and ndexed wthn the mage, mage segmentaton technque are used [4] However, segmentaton results of general mages are nosy and contan too many regons To ndex an mage usng ts content, there are currently three general Manuscrpt receved November 5, 2010 Manuscrpt revsed November 20, 2010

2 153 approaches: obect recognton, statstcal analyss and mage segmentaton Current CBIR systems such as IBM's QBIC [14], allow automatc retreval based on smple characterstcs and dstrbuton of color, shape and texture But they do not consder structural and spatal relatonshps and fal to capture meanngful contents of the mage n general Also the obect dentfcaton s sem-automatc The Chabot [15] proect ntegrates a relatonal database wth retreval by color analyss Textual meta-data along wth color hstograms form the man features used Vsual seek allows query by color and spatal layout of color regons Text based tools for annotatng mages and searchng s provded Obect recognton technques are lmted to specfc domans, eg, mages contanng smple geometrc obects Ths approach has been used to retreve mages of tools and CAD geometrc obects [12] and also medcal mages [13] For most other types of mages, such as mages contanng people, sceneres, etc, Obect recognton technques are nfeasble To pursut the complexty of these mages, researches then employed statstcal ndexng based on color and texture [16], [17] Images can be retreved by specfyng a combnaton of RGB color values, textural measures, and more recently usng other features such as shapes and spatal relatons between regons n the mage [18], [5], [19] Thus, ths approach are stll lmted to smple obects, and thus for general mages currently do not provde a meanngful obect based representaton 2 ARCHITECTURE OF PROPOSED SYSTEM The dalog box shows the features extracted from an mage and the measured category for that mage The category s obtaned automatcally by analyzng the composton of color, texture and structure from the man regons The regons are produced usng the percepton-based mage segmentaton system [6] By mplementng perceptual groupng, the results acheved are clean and only contanng sgnfcant regons The proposed categores were chosen to provde suffcent groupng of mages wth smlar themes, usng general features The doman assumed s photographc mages, but wth an unlmted range of themes nature, people, buldng, etc The semantc categores are provded to detect most common types of mages, such as those exst on the nternet The sngle obect category s for mages contanng only two regons, conformng to the Gestalt fgure/ground prncple Query specfcaton Rankng Vsualzaton Texture People Indexng /Matchng Structure Categores Segmentaton Groupng and domnant regon Shape Image nserton Color Landscape Fgure 1 shows archtecture of a content-based mage retreval system usng mean shft algorthm Three man functonaltes are supported: Data nserton, Categorzaton and Query processng The data nserton subsystem s responsble for extractng approprate features from mages results from ths wll be classfed nto four dfferent general and two semantc categores and storng them nto the mage database Ths process s performed off-lne The query processng, ntern, s organzed as follows: the nterface allows a user to specfy a query by means of a query pattern and to vsualze the retreved smlar mages The query-processng module extracts a feature vector from a query pattern and apples a metrc as the Eucldean dstance to evaluate the smlarty between the query mage and the database mages Next, t ranks the database mages n a decreasng order of smlarty to the query mage and forwards the most smlar mages to the nterface module The database mages are ndexed accordng to ther feature vectors to speed up retreval and smlarty computaton Note that both the data nserton and the query processng functonaltes use the feature vector extracton module Category Name Landscape People Shape domnant Color domnant Texture domnant Structure domnant Fg1 Archtecture of new system Table 1 Image categores Feature Characterstcs Colors green & blue Spatal relaton n vertcal layers Human skn hue 2 regons foreground/background mage Shapes are non-complex More than 2 regons Color dstrbuton smooth (small varance) More than 2 regons Color dstrbuton non-smooth (large varance) More than 2 regons Shapes complex Contans eometrc obects

3 154 3 IMAGE SEGMENTATION The local color feature extracton starts wth color mage segmentaton For mage segmentaton, we use mean shft algorthm [7] Here, color clusterng s performed on each mage to obtan regons After segmentaton, only small number of color remans Informaton lke number of regons, tme taken to segment an mage, boundary ponts, regon ponts, and regon numbers can be extracted Large classes of mage segmentaton algorthms are based on feature space analyss In ths paradgm the pxels are mapped nto a color space and clustered, wth each cluster delneatng a homogeneous regon n the mage Pxels were drectly assocated wth the mode to whch the path converged The approxmaton does not yeld a vsble change n the fltered mage Recursve applcaton of the mean shft property yelds a sample mode detecton procedure The modes are the local maxma of the densty They can be found by movng at each teraton the wndow by the mean shft vector, untl the magntude of the shfts becomes less than a threshold The procedure s guaranteed to converge The number of sgnfcant modes detected automatcally determnes the number of sgnfcant clusters present n the feature space 31 Mean Shft Algorthm 1 Choose the approprate radus r of the search wndow 2 Choose the ntal locaton of the wndow 3 Compute the mean shft vector 2 MSV= r p( x) p + 2 p( x) Where x s the p-dmensonal feature vectors, p(x) s the probablty densty functon of x and p(x) s the gradent of p(x) 4 Translate the search wndow by the shft amount 5 Repeat tll convergence Gestalt prncples of proxmty, smlarty and good contnuaton These rules wll be used n groupng ntal segments, to produce sgnfcant regons n the mage The frst groupng performed s the sze groupng The am of ths groupng s to merge nosy areas (regon whose sze s less than 100) usng the smlarty of regon The next groupng stage s the color hstogram groupng Ths s performed by comparng the smlarty of two regon color hstograms Ths s then followed by lne contnuaton groupng Regons are grouped based on comparng lne contnuaton surroundng regons The am domnant regon extracton s to elmnate background, non-mportant regons, producng the most essental regon The reducton of non useful regons s requred to reduce the matchng and expensve structural analyss The background wll be elmnated by applyng the fgure/background prncple of Gestalt laws By analyzng that the largest regon surroundng other obects entrely can conclude that ths regon s the background thus elmnated In the proposed system, domnant regon wll be extracted automatcally by analyzng the sze (largest), locaton (center) of the regons and appearance of nterestng shapes and structures Interestng shapes wll be udged on the regon shape regularty and ts geometrc propertes A fve-step process s performed n the segment mergng and domnant regon extracton stage: 1 The mage map s a two dmensonal array, wth the same proportons as the mage It contans the value representng the segment each pxel belongs to, at the correspondng pont n the array 2 Segment nformaton s calculated from the mage map Ths ncludes nformaton about the average color of the segment and ts sze Ths nformaton s requred before any segment mergng can begn 3 A lst of neghborng segments s recorded for each segment n the mage Ths lst s used to determne the possble segments that the segment can be merged wth 4 Sze mergng s performed 5 The domnant regon s extracted from the mage 4 CATEGORY GENERATION Fg 3 (a) Orgnal mage (b) Mean shft flterng result 32 Regon Groupng and Domnant Regon Extracton To mplement Gestalt laws [6] nto our proposed groupng scheme, we separate them nto two categores: local and global measure rules Local measure rules are based on Category generaton s responsble for assgnng the mage to one of the categores, wth the category amng to provde suffcent groupng of mages wth smlar characterstcs These descrptors are based on a number of features that can be easly detected n mages, but are often best found va the applcaton of a non-generc mage analyss technque (e texture domnant mages are handled most effectvely wth robust texture matchng, whereas shape domnant mages are compared wth good shape matchng)

4 155 The shape domnant category s for mages contanng only two regons, wth smple, regular shapes and for those conformng to the Gestalt fgure/background prncple The color domnant category s for mages contanng regons wth a smooth color dstrbuton and less regular shapes The texture domnant category s for mages that contan hghly textural regons (usng the procedure proposed n [11]) The structure domnant category s for mages that nclude straght lnes, geometrc shapes or conform to a structural template The people category s for mages wth promnent regons wth a hue n the human skn range The landscape category s for pctures of landscapes that obey the landscape template and have regons wth colors n the sky, land and sea color ranges matches n decreasng order of smlarty along wth ther relatve nformaton 5 QUERY INTERFACE Here, we descrbe cluster-based ndexng method amng to speed-up the evaluaton of range queres Ths s carred out by reducng the number of canddate mages, e mages on whch the optmal regon-matchng problem has to be solved The procedure s as follows: 1 Gven n the number of query regons, for each query regon q, fnd the regons belongng to cluster c, where = 1 n, 2 For each regon r n the mage database a Fnd the feature vector f, for regon r b For each query regon q, n the query set, Fnd query feature vector f, for q Fnd the Eucldean dstance between d = m ( f k f k ) K = 1 2 f, and f usng: where m s the dmenson of the feature vector Ths score s zero f the regons features are dentcal, t ncreases as the match becomes less perfect Measure the smlarty between f and f usng μ = d τ, where s the search range lmt set by user v If μ 0, then f belongs to cluster c and go to step 2 After the completon of the above procedure, we ndex the mages Once the user selects the query, we apply range search on the tree and the selected regons belongng to mage are retreved as resultant set Members of resultant set s ranked accordng to overall score and return the best 6 CONCLUSION Fg5 Result after comparson Ths research ams to develop an mage retreval system that extracts the domnant regon n an mage, placng the mage nto one or more categores and search mage wth the help of Eucldean dstance These categores are developed from an understandng of how psychologcal prncples apply to computer vson, extenson of the proposed work some results such as people classfer are naccurate Ths research wll provde a new mage retreval system that provdes users wth the ablty to further classfy the content of an mage and search mages The mpact from ths method s more accurate retreval results An mage wll be represented by rch descrptons that relate drectly to the content of the mage However, the dea of categorzaton and mage search has been fulflled n ths paper 7 FUTURE WORK In future by ths work database can be classfed In ths classfed database retreval wll be effcent In ths method many classes of database wll be created correspondng to each basc color Wth the development of CBIR, however, there are many problems One s that snce dfferent researchers use dfferent feature spaces, or use the same feature space but dfferent descrpton, then the measure s dfferent, t s dffcult for retreval to be unversal, especally n WEB In future t can be enhanced to retreval on nternet or WEB Browsng Currently, some results such as people classfer are naccurate Many mages of people encountered have poor color qualty and nosy Thus, more robust classfer s requred We are usng weghtng method for categorzaton we can use better weghtng, prorty and herarchcal system wthn the proposed categores Investgate a better classfer for dfferent

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