Journal of Communcatons Vol. 0 No. January 0 Gradng Image Retreval Based on DCT and DWT Compressed Domans Usng Low-Level Features Chengyou Wang Xnyue Zhang Rongyang Shan and Xao Zhou School of echancal Electrcal and Informaton Engneerng Shandong Unversty Weha 6409 Chna Emal: wangchengyou@sdu.edu.cn; zxyxnyue@6.com; sdusry@6.com; zhouxao@sdu.edu.cn Abstract Nowadays the majorty of mages are n JPEG and PEG compressed formats and JPEG000 s consdered to be the next generaton of compresson standard due to the hghperformance of dscrete wavelet transform (DWT). It s tmeconsumng and occupes too much memory n conventonal mage retreval ways. In order to solve these problems we use gradng retreval technques to mplement mage retreval based on dscrete cosne transform (DCT) compressed doman and DWT compressed doman. For mage retreval based on DCT doman we use color features: color moment and color hstogram to descrbe content of mages and propose a new dynamc color space quantzaton based on color dstrbuton; For mage retreval based on DWT doman we use texture features as two level feature vectors. The mean and standard devaton of low frequency sub-band coeffcents are used as the frst level retreval. The means and standard devatons of selected hgh frequency sub-band coeffcents are used as the second level retreval. Furthermore the thrd level retreval s acheved by the fast wavelet hstogram. Our experment results clearly show that the two gradng mage retreval algorthms work better than other algorthms: store memory s reduced and retreval accuracy s mproved. Index Terms Content based mage retreval (CBIR) compressed doman dscrete cosne transform (DCT) dscrete wavelet transform (DWT) color features texture features I. INTRODUCTION The query condtons of content based mage retreval (CBIR) [] are mages or the descrptons of mages. We can extract mage features and fnd the approxmate mages by smlarty measure algorthms. However wth appearance of compresson standards the applcaton of compressed mages has become common. Fg. shows the codng and decodng processes of mages. Image retreval technque based on pxel doman extracted the mage features at pont but mage retreval technques based on compressed doman [] extracted the mage features at pont 0 pont or pont. It can be seen that mage retreval technques based on compressed doman omt fully decodng and save the spendng on equpments anuscrpt receved August 04; revsed January 0 0. Ths work was supported n part by the promotve research fund for excellent young and mddle-aged scentsts of Shandong Provnce Chna under Grant No. BS0DX0 and the Natonal Natural Scence Foundaton of Chna under Grant No. 607. Correspondng author emal: wangchengyou@sdu.edu.cn. do:0.70/jcm.0..64-7 and reduce the quantty of calculaton. In ths paper we extracted mage features at pont. orgnal mage reconstructed mage mappng transform nverse transform codng quantzaton decodng nverse quantzaton entropy codng entropy decdong pont pont pont pont 0 Fg.. Image codng and decodng. transmsson Recently the research on mage retreval based on compressed doman has focused on transform doman manly such as dscrete Fourer transform (DFT) doman dscrete cosne transform (DCT) doman and dscrete wavelet transform (DWT) doman. Stone and L proposed extractng mage features by DFT coeffcents and controllng smlarty measure of lumnance and texture by two thresholds []. Lu et al. n [4] proposed an mage retreval scheme n the DCT doman that s sutable for retreval of color JPEG mages of dfferent szes. Smth and Chang proposed extractng mage features by the mean and standard devaton of DCT coeffcents [] and reducng the dmensons of characterstc vector by Fsher dscrmnate analyss (FDA) [6]. Baharudnl proposed extractng texture features by hstograms whch are constructed by drect current (DC) coeffcents and partal alternatng current (AC) coeffcents [7]. Lay proposed energy hstogram based on DFT coeffcents [8]. Eom proposed DCT based edge hstogram [9]. In [0] a novel evolutonary method called evolutonary group algorthm (EGA) was proposed for complcated tme-consumng optmzaton problems based on content-based mage ndexng algorthms. Zhang proposed extractng the contour of bnary mages at pont and descrbng mage features by nvarant moments []. Smth proposed usng wavelet hstogram technques to extract texture features []. Albanes proposed extractng texture features by usng correlaton n wavelet sub-bands []. The retreval method based on Hadamard matrx and DWT (HDWT) proposed by Fars and ohamadzadeh n [4] has been dscussed. In ths paper we study mage retreval based on DCT compressed doman and DWT compressed doman. We realze mage retreval based on DCT compressed doman by gradng retreval and propose dynamc color space quantzaton algorthm based on color dstrbuton to 0 Engneerng and Technology Publshng 64
Journal of Communcatons Vol. 0 No. January 0 reduce dmensons of color hstograms; we realze mage retreval based on DWT compressed doman by gradng retreval and propose a new retreval method whch uses the mean and standard devaton of low frequency wavelet sub-band coeffcents and selects hgh frequency wavelet sub-band coeffcents as two level feature vectors. The organzaton of the paper s as follows. The technques we used are provded n Secton II. The specfc detals to realze mage retreval based on DCT compressed doman and DWT compressed doman are presented n Secton III and Secton IV respectvely. Expermental results of algorthms we proposed and analyss are presented n Secton V. Concluson of ths paper s presented n Secton VI. II. FEATURE VECTORS BASED ON DCT DOIAN AND DWT DOAIN Ths secton ntroduces feature vectors based on DCT doman and DWT doman whch we use to descrbe the content of mages. A. Feature Vectors Based on DCT Doman ) Color moment We choose color moment [] as the frst level feature vector. If the color value at pont ( xy ) s p( x y ) the frst two moments namely the mean and the standard devaton of color are: N N E p x y p x y E N x y N x y ( ) ( ) () ) Color hstogram We use color hstogram [6] to descrbe the content of mages to mprove retreval accuracy. The defnton of the color hstogram s: n h () () n where n represents the number of pxels of the -th color n represents the number of pxels of mages. B. Feature Vectors Based on DWT Doman ) Wavelet sub-band mean and standard devaton The low-frequency wavelet sub-band and hghfrequency wavelet sub-band descrbe the outlne and detals of mages respectvely. We can calculate the mean E and standard devaton of wavelet sub-band as texture feature. If the wavelet sub-band coeffcent value at pont ( xy ) s W( x y ) they can be defned as: N N E W x y W x y E N x y N x y ( ) ( ) () ) Wavelet hstogram We use fast wavelet hstogram technques [7] to construct wavelet hstogram to descrbe the texture feature of mages. We assume that mages are decomposed by -level wavelet transform durng mage codng. Frstly calculate energy of each wavelet subband n every pont: xy W( x y) (4) where xy denotes the energy at pont (x y). Secondly In order to mnmze the computatonal complexty take level for example each wavelet subband s down-sampled nto -level wavelet sub-band sze to obtan texture channel. Every texture pont s changed nto 9-dmensonal vector by consderng texture channels are generated from other 8 sub-bands. Thrdly each component of 9-dmensonal vector s threshold to two levels hgh () or low (0). It can be defned as: B 0 T( ) ( 9) T( ) where T s the 9-dmensonal vector at pont (x y) n level s the medan of every texture channel B s the result of vector bnaryzaton. Fourthly represent each 9-dmensonal vector L by egenvalue V : V L L L () 8 7 0 (9) (8) () (6) Count the occurrence frequency of each egenvalue V to construct hstogram. III. IAGE RETRIEVAL BASED ON DCT COPRESSED DOAIN We proposed -level gradng mage retreval algorthm to realze mage retreval based on DCT compressed doman. We used color features: color moments and color hstogram to descrbe content of mages n addton we proposed dynamc color space quantzaton algorthm based on color dstrbuton to mprove retreval accuracy. Fg. shows the dagram of retreval based on DCT compressed doman. compressed mage stream approxmate mages entropy decodng nverse quantzaton retreval n database color hstogram color moment the second level retreval the frst level retreval calculate approxmate value at pont ( xy ) Fg.. Retreval based on DCT compressed doman. x40 x4 x4 x4 x 0 x 6 0 x70 x7 x7 x7 x x 4 x 6 x 64 calculate color hstogram x00 x0 x0 x0 x04 x0 x06 x07 x 0 x 0 Fg.. Partton sketch. 00 x0 x x x x x 4 x x 4 0 x 7 x 7 x4 x x6 x7 x44 x4 x46 x47 x 7 x 67 x74 x7 x76 x77 calculate color moment Feng [8] dvded each 8 8 mage block nto four 4 4 mage blocks and held that the color values n each 0 Engneerng and Technology Publshng 6
Journal of Communcatons Vol. 0 No. January 0 4 4 block are consstent. He used the average color value of each 4 4 block 00 0 0 to represent the color values of the entre 4 4 mage blocks as shown n Fg.. He used the top left 4 DCT coeffcents F (00) F (0) F (0) F () of each 8 8 DCT block to calculate 00 0 0 : 00 0 0 F(00) F(0) F(0) F() 6 F(00) F(0) F(0) F() 6 F(00) F(0) F(0) F() 6 F(00) F(0) F(0) F() 6 A. The Frst Level Retreval ) The frst level feature vector We use the algorthm above-mentoned to calculate the approxmate color value of mages and extract color moments as the frst level feature vector. The functon of color moments s to dscard mages whch are not consstent wth the query mage n color extremely. We use low dmensonal color moments as the frst level feature vector gven that the functon of the frst level feature vector s prelmnary screenng. In order to reduce retreval tme we use the frst two color moment formula () shows ther defntons as the frst level feature vector to reduce retreval tme further. In ths way for color mages whch have three color channels the frst level feature vector s: (7) [ E E E ] (8) where E E E represent mean values of three color channels respectvely; represent standard devaton of three color channels respectvely. ) Smlarty measure For the frst level feature vector we use block dstance [9] to measure the smlarty between feature vector I : N D( I ) I (9) The JPEG mages use YCbCr color space and we assgn dfferent weghts to each color channel gven that we are more senstve to Y channel than Cb channel and Cr channel. The weghts are 0.6 0. and 0. respectvely. B. The Second Level Retreval ) The second level feature vector In order to mprove retreval accuracy we use color hstogram as the second level feature vector. The dmensons of color space decdes the dmensons of color hstograms. j j j If the dmensons of hstograms s too hgh we need more memory to storage feature vectors and longer retreval tme due to the huge calculaton; on the contrary f the dmensons of hstograms s too low the color hstogram can t be used as an effectve feature vector and the retreval accuracy decreases. We propose a dynamc color space quantzaton algorthm based on the mage color dstrbuton. We use Elephant mage as an example and count frequency of each color occurrng n the mage as shown n Fg. 4 and Fg. 4 respectvely. It can be seen from Fg. 4 that the occurrence frequency of colors n area L s hgher than colors n area L L. The colors n area L have a greater effect on retreval result due to the hgher occurrence frequency. We defne area L as man color nterval and defne area L L as mnor color ntervals. The central dea of algorthm s that we choose smaller quantzaton ntervals for man color ntervals and bgger quantzaton ntervals for mnor color ntervals to realze non-unform quantzaton. For dfferent mages whch have dfferent color dstrbutons we can partton dfferent quantzaton ntervals to acheve the goal of dynamc quantzaton. L L L Fg. 4. Elephant mage: Orgnal mage Color dstrbuton. The concrete steps of quantzaton algorthm are as follows: Step. Count the occurrence frequency of each color and save n vector h. The calculaton formula s: n h ( ) 6 (0) N where n s the number that the -th color occurs n mage and N s the number of pxels. Step. Color classfcaton. We dvde colors nto two types: man color and mnor color. We set threshold q for color classfcaton. If the occurrence frequency of one color C ( 6 ) s larger than or equal to q we defne t as man color; on the contrary we defne t as mnor color. The dscrmnaton formula s: 0 Engneerng and Technology Publshng 66
Journal of Communcatons Vol. 0 No. January 0 man color h( ) q C () mnor color h( ) q. If we nterpret color value at pont ( xy ) as random varable and assume that the occurrence of 6 knds of colors s equprobable we can prelmnary set threshold q 0.004. 6 Step. Fnd man color ntervals. If the most colors n one nterval are man colors we defne t as man color nterval; on the contrary we defne t as mnor color nterval. An mage may have several man color ntervals as shown n Fg.. It can be seen from Fg. that the occurrence frequency of colors n area L and L 4 s hgher than colors n area L L and L. Thus we can defne area L and L 4 as man color ntervals and defne L L and L as mnor color ntervals. In ths paper we assume that an mage has one man color nterval or two man color ntervals due to that t wll be dffcult to conduct smlarty measure f we partton the color space too elaborate. We set a flag f f m fm t ndcates that the mage has two man color quantzaton ntervals; f fm 0 t ndcates that the mage has one man color quantzaton nterval. In addton we need to notce that mnor color ntervals may contan some man colors we choose to gnore them because the number of man colors n mnor ntervals s small. ntervals. For example n Fg. f we have found man color ntervals and mnor color ntervals and s s s s 4 s respectvely are the startng ponts of L L L L 4 L they may be not the multple of 8. We need to deal wth these startng ponts to make them to be dvsble by 8: s cel ( s /8) 8 () where cel () functon returns a value of number rounded upwards to the nearest nteger. In addton we should set a fxed length of man color ntervals n order to make dfferent mages have same dmenson of color hstogram. We count the number of color whose occurrence frequency s greater than threshold q n dfferent mages and the value are 04 manly. Thus the total length of man color ntervals s 04 n ths paper. In ths way the mage has 04 6 04 4 quantzaton ntervals. 4 8 Step. Calculate the second level feature vector. If s and e are the startng pont and the endng pont of the - th quantzaton nterval respectvely the frequency of the -th quantzaton nterval s: e h( ) h( n) 4 () ns ) Smlarty measure If we use the dynamc algorthm above-mentoned to conduct color space quantzaton for dfferent mages the components of color feature vector n the same place may not correspond to the same color. For example we quantze color space for a Lake mage and a Cherry mage respectvely as shown n Fg. 6. We lst only startng ponts of quantzaton ntervals to represent quantzaton ntervals. L L L 4 L L Fg. 6. Lake mage Cherry mage. Fg.. Zebra mage: Orgnal mage Color dstrbuton. Step 4. Quantzaton ntervals partton. The colors n man color ntervals have a greater effect on smlarty measure. Thus we should choose a smaller nterval for the man color ntervals to mprove retreval accuracy. In the same way we should choose a bgger nterval for mnor color ntervals to reduce store memory and mprove retreval effcency. In ths paper the ntervals respectvely are 4 and 8. Thus startng ponts must be a multple of 8 both man color ntervals and mnor color Lake mage: [ 9 7 9 7 4 4 49 7 6 6 69 7 77 8 8 89 9 97 00 9 7 4 6 69 77 8 9 0 09 7 4 49]; Cherry mage: [ 9 7 7 4 4 49 7 6 6 69 7 77 8 8 89 9 97 0 0 09 7 9 7 4 6 69 77 8 9 0 09 7 4 49]. In order to solve ths problem we only consder the overlappng quantzaton ntervals between mages. For example we could see that the quantzaton ntervals of these two mages n Fg. are dfferent but they have overlappng quantzaton ntervals: [6] [740] 0 Engneerng and Technology Publshng 67
Journal of Communcatons Vol. 0 No. January 0 [444] [448] [4] [6] [760] [664] [668] [697] [776] [7780] [88] [899] [996] [9700] [004] [744] [4] [60] [668] [6876] [7784] [89] [900] [008] [096] [74] [] [40] [448] [46]. Thus we can use the color hstogram of overlappng parts to calculate dstance between mages. The concrete steps of smlarty measure are as follows: Step. Compare flags of dfferent mages. We use f m whose value represents the number of color ntervals of an mage to screenng. If flags f m are equal we can take next step; on the contrary we should dscard mage drectly. Step. Calculate the overlappng quantzaton ntervals between mages. Thus we should save the startng ponts the feature vector of query mage compare flags f m equal calculate ntersecton between mages of quantzaton ntervals nto 4-dmensonal vector S when calculatng color hstogram of an mage. In ths paper we save only the startng ponts of quantzaton ntervals of Y component due to that the quantzaton of three components: Y Cb Cr are the same. Step. Calculate dstance between mages. We use hstogram ntersecton to calculate dstance between mages: D( I ) mn( I j j) j A (4) Set Α save the number of the overlappng quantzaton ntervals. We assgn dfferent weghts to each color channel gven that we are more senstve to Y channel than Cb channel and Cr channel. The weghts are 0.6 0. and 0. respectvely. Fg. 7 shows the dagram of smlarty measure. calculate dstance between mages approxmate mages Fg. 7. The dagram of smlarty measure. nequal dscard compressed mage stream entropy decodng nverse quantzaton calculate the mean and standard devaton of some sub-bands approxmate mages the thrd level retreval retreval n database the second level retreval the frst level retreval construct wavelet hstogram Fg. 8. Retreval based on DWT compressed doman dagram. IV. IAGE RETRIEVAL BASED ON DWT COPRESSED DOAIN We propose -level gradng mage retreval algorthm to realze mage retreval based on DWT compressed doman. We use texture features: the mean and standard devaton of wavelet sub-band and wavelet hstogram to descrbe content of mages. In addton we propose usng the mean standard devaton of low frequency sub-band and the mean standard devaton of hgh frequency subbands as two level feature vectors. Fg. 8 shows retreval based on DWT compressed doman dagram. A. The Frst Level Retreval ) The frst level feature vector The low frequency of wavelet sub-band descrbes the outlne of mages. Thus we can use the mean and standard devaton of low frequency nformaton as the texture feature vector. Formula () shows ther defntons. In ths way for color mages whch have three color channels the frst level feature vector s: [ E E E ] () ) Smlarty measure Lke the frst level feature vector n DCT doman we use block dstance to measure the smlarty between the frst level feature vectors n DWT doman and assgn dfferent weghts 0.6 0. 0. to Y channel Cb channel and Cr channel respectvely. B. The Second Level Retreval ) The second level feature vector The hgh frequency of wavelet sub-bands descrbes the detals of mages: horzontal edge vertcal edge and dagonal edge. Thus we can use the mean and standard devaton of hgh frequency nformaton as the texture feature vector to mprove retreval accuracy further. For an mage decomposed by -level wavelet transform t has one low frequency sub-band LL and 9 hgh frequency sub-bands LH HL HH LH HL HH LH HL HH. Thus the feature vector of low frequency sub-band has 6 dmensons and the feature vectors of hgh frequency sub-bands have 9 4 dmensons. The former algorthm uses the mean and standard devaton of all sub-bands as the same level feature vector. The feature vector wll have 60 dmensons and the retreval tme s longer. We can see that the dmenson of the hgh frequency sub-bands s hgher and the dmenson of the low frequency sub-band s lower. Thus we propose that after the prelmnary screenng by the feature vector of low frequency wavelet sub-band we can conduct further screenng by the feature vectors of hgh frequency wavelet sub-bands. Because lots of mages whch do not meet the condtons are dscarded accordng to the lower dmenson feature vector we only need to measure the smlarty between mages whch meet the condtons of the frst level retreval and 0 Engneerng and Technology Publshng 68
Journal of Communcatons Vol. 0 No. January 0 the retreval tme s shorter. In addton we can set dfferent thresholds to these two level feature vectors the retreval accuracy can be mproved. As we know sub-bands LH n HL n HH n descrbe the horzontal edge vertcal edge and dagonal edge of mages respectvely. Takng horzontal edge nformaton as example we can choose only sub-bands LH LH to wavelet coeffcents wavelet coeffcents calculate the feature vectors of all wavelet sub-bands calculate the feature vectors of selected wavelet sub-bands descrbe. In addton f level n s lower the sub-band coeffcents correspond to t are quantzed as 0 manly and they have less effect on retreval result. Thus n order to reduce feature space we can use only the means and standard devatons of sub-bands LH HL HH LH HL HH as second level feature vectors. Fg. 9 shows the dfference between these two algorthms. feature vectors of wavelet sub-bands retreval n database feature vector of low frequency wavelet sub-band the frst level retreval Fg. 9. Retreval algorthm n reference [] Retreval algorthm n ths paper. approxmate mages retreval n database feature vectors of hgh frequency wavelet sub-bands the second approxmate mages level retreval HH dscard onestage -D mage DWT LL onestage DWT LL onestage DWT HL LH HH HL LH HH HL LH dscard dscard B B B B B B B B B B 4 B B 6 V B 4 B B B4 B 0 B 6 count wavelet hstogram occurrence frequency of V LL dscard Fg. 0. Schematc of fast wavelet hstogram at level. ) Smlarty measure We use block dstance to measure the smlarty between the second level feature vectors and assgn dfferent weghts 0.6 0. 0. to Y channel Cb channel and Cr channel respectvely. C. The Thrd Level Retreval ) The thrd level feature vector In order mprove retreval accuracy we use fast wavelet hstogram technques to construct wavelet hstogram to descrbe texture feature of mages further. As we mentoned n Secton II we assume that mages are decomposed by -level wavelet transform durng mage codng. In order to mnmze calculaton we set level k n other words -level wavelet sub-bands and -level wavelet sub-bands should be down-sampled to have the same sze wth -level wavelet sub-band and the sample rates are 4 4 and respectvely. In order to reduce feature space sub-band LL s not ncluded n calculaton range due to that the frst level feature vector whch extracted by low frequency coeffcents s suffcent to descrbe the low frequency nformaton of mages so we have no need to consder sub-band LL repeatedly. Thus as the second level feature vectors we can consder only sub-bands LH HL HH LH HL HH to construct wavelet hstogram. Fg. 0 shows the dagram of constructng wavelet hstogram. ) Smlarty measure We use block dstance to measure the smlarty between the thrd level feature vectors and assgn dfferent weghts 0.6 0. 0. to Y channel Cb channel and Cr channel respectvely. V. EXPERIENTAL RESULTS AND ANALYSIS In order to verfy the valdty of the algorthms we proposed we download 600 mages from http://www.cs.washngton.edu/research/magedatabase and http://wang.st.psu.edu/docs/related ncludng 00 JPG mages and 00 BP mages. Our database contans 0 knds of mages lke Polar bear Lake Football feld; each contans 0 JPG mages and 0 BP mages. Due to the lack of wavelet compressed mages we use BP mages to smulate the wavelet coeffcents. The BP mages are transformed by -level wavelet decomposton usng bor4.4. So we get the wavelet coeffcents. Our software envronment of experment s ATLAB 7.0. We use the recall rato [0] and the precson rato [0] to evaluate retreval results. Recall rato R s the rato between the number of the rght approxmate mages n retreval result n and the number of rght approxmate mages n database n I ; Precson rato P s the rato between the number of the rght approxmate mage n retreval result n and the number of all mages n retreval result n R. They can be respectvely expressed as: 0 Engneerng and Technology Publshng 69
Journal of Communcatons Vol. 0 No. January 0 R n n P ni nr Thus the algorthm we proposed can mprove retreval accuracy. In addton the second level feature vectors have 4 4 8 dmensons n our algorthm; f we use unform-quantzaton algorthm and set nterval as 4 the second feature vectors have (6 4) 9 dmensons. Thus the algorthm we proposed also saves store memory. Secondly we use -level gradng mage retreval algorthm based on DCT compressed doman mentoned n Secton III to conduct gradng retreval. Selectng a Football feld and a Lake mage at random as example Fg. and Fg. show ther retreval results respectvely and the frst mage s query mage. We count the recall and precson ratos of 0 knds of JPG mages respectvely as shown n Table I. (6) A. Expermental Results and Analyss Based on DCT Compressed Doman In Secton III we propose a dynamc color space quantzaton algorthm to reduce the dmenson of color hstogram. Frstly we only use hstogram as feature vector to conduct retreval to verfy the valdty of the quantzaton algorthm. Selectng a Polar bear mage at random as an example we use the quantzaton algorthm and the smlarty measure mentoned to conduct retreval. Fg. shows the result and the frst mage s query mage. In reference [6] the color space s quantzed by unform-quantzaton algorthm and we set quantzaton nterval as 4. Fg. shows the retreval result based on the algorthm we proposed. Fg.. Retreval result of Football feld mage Retreval result of Lake mage. TABLE I: RECALL AND PRECISION RATIOS OF 0 KINDS OF JPG IAGES Fg.. Retreval result based on reference [6] Retreval result based on the algorthm we proposed. Images Recall rato (%) Precson rato (%) Cherry Lake 80 70 90 Sky 40 8 Zebra 97 Lon 60 90 Elephant 0 88 As shown n Fg. the recall rato R s: Dnosaur 90 4 00% 80% 0 The precson rato P s: 4 00% 80% 0 Polar bear 80 Football feld 8 9 Bus 40 8 (7) (8) B. Expermental Results and Analyss Based on DWT Compressed Doman In Secton IV we propose to use the mean standard devaton of low frequency wavelet sub-bands LL and the means and standard devatons of hgh frequency wavelet sub-bands LH HL HH LH HL HH as two level feature vectors. In reference [] they used the As shown n Fg. the recall rato R s: 0 00% 00% 0 (9) The precson rato P s: 0 00% 00% 0 0 Engneerng and Technology Publshng (0) 70
Journal of Communcatons Vol. 0 No. January 0 mean and devaton of all wavelet sub-bands as the same level feature vector. Selectng Cherry mages at random as example Fg. shows the retreval results of these two algorthms respectvely. Fg. 4. Retreval result of Bus mage Retreval result of Dnosaur mage. TABLE II: RECALL AND PRECISION RATIOS OF 0 KINDS OF BP IAGES Images Cherry Lake Sky Zebra Lon Elephant Dnosaur Polar bear Football feld Bus Fg.. Retreval result based on reference [] Retreval result based on the algorthm we proposed. As shown n Fg. the recall rato R s: 00% 70% 0 () The precson rato P s: 00% 70% 0 VI. CONCLUSION We have presented two gradng retreval algorthms based on DCT compressed doman and DWT compressed doman respectvely. Frstly we use -level gradng mage retreval algorthm to realze mage retreval based on DCT compressed doman. We used color features: color moment and color hstogram to descrbe content of mages. For the second level feature vector color hstogram nstead of quantzng color space by unform quantzaton algorthm we use a new dynamc color space quantzaton algorthm based on color dstrbuton to reduce dmensons of hstogram. Our expermental results clearly show that the -level gradng mage retreval algorthm works better than other algorthms: store memory s reduced and retreval accuracy s mproved. Secondly we use -level gradng mage retreval algorthm to realze mage retreval based on DWT compressed doman. We used texture features: the mean and standard devaton of wavelet sub-band and fast wavelet hstogram to descrbe content of mages. Instead of usng the mean and standard devaton of all wavelet sub-bands as the same level feature vector we use the mean standard devaton of low frequency sub-band and the means standard devatons of some selected hgh frequency sub-bands as two level feature vectors. Our expermental results clearly show that the two gradng () The precson rato P s: 6 00% 84% (4) Thus the algorthm we proposed can mprove retreval accuracy. In addton the retreval tme of the algorthm n reference [] s.76s; the retreval tme of the algorthm we proposed s 0.s. Besdes the dmenson of feature vector used the algorthm n reference [] to extract s 9 60 ; the dmenson of feature vector used the algorthm to extract s 6 4. Thus the algorthm we proposed also saves store memory. Secondly we use -level gradng mage retreval algorthm based on DWT compressed doman mentoned n Secton IV to conduct gradng retreval. Selectng a Bus and a Dnosaur mage at random as example Fg. 4 and Fg. 4 show ther retreval results respectvely and the frst mage s query mage. We count the recall and precson ratos of 0 knds of BP mages respectvely as shown n Table II. 0 Engneerng and Technology Publshng Precson rato (%) 87 9.7 98 8 00 97 96 () As shown n Fg. the recall rato R s: 6 00% 86.7% 0 Recall rato (%) 80 70 80 40 66 4 90 80 90 60 7
Journal of Communcatons Vol. 0 No. January 0 mage retreval algorthms work better than other algorthms: store memory s reduced and retreval accuracy s mproved. ACKNOWLEDGENT Ths work was supported n part by the promotve research fund for excellent young and mddle-aged scentsts of Shandong Provnce Chna under Grant No. BS0DX0 and the Natonal Natural Scence Foundaton of Chna under Grant No. 607. The authors would lke to thank Xaoyan Wang and Fanfan Yang for ther knd help and valuable suggestons. The authors would lke to thank the anonymous revewers and the edtor for ther valuable comments to mprove the presentaton of the paper. REFERENCES [] T. Dharan and I. L. Aroquaraj A survey on content based mage retreval n Proc. Int. Conf. on Pattern Recognton Informatcs and oble Engneerng Salem Tamlnadu Inda Feb. - 0 pp. 48-490. [] D. Edmundson and G. Schaefer An overvew and evaluaton of JPEG compressed doman retreval technques n Proc. 4th Int. Symp. on Electroncs n arne Zadar Croata Sep. -4 0 pp. 7-78. [] H. S. Stone and C. S. L Image matchng by means of ntensty and texture matchng n the Fourer doman n Proc. SPIE: Storage and Retreval for Stll Image and Vdeo Databases IV San Jose CA USA vol. 670 Feb. - 996 pp. 7-49. [4] Z.. Lu S. Z. L and H. Burkhardt A content-based mage retreval scheme n JPEG compressed doman Internatonal Journal of Innovatve Computng Informaton and Control vol. no. 4 pp. 8-89 Aug. 006. [] J. R. Smth and S. F. Chang Transform features for texture classfcaton and dscrmnaton n large mage databases n Proc. IEEE Int. Conf. on Image Processng Austn TX USA vol. Nov. -6 994 pp. 407-4. [6] H. uhlenben and T. ahng FDA - A scalable evolutonary algorthm for the optmzaton of addtvely decomposed functons Evolutonary Computaton vol. 7 no. 4 pp. -76 Dec. 999. [7] F. E. alk and B. Baharudn Effectve content-based mage retreval: Combnaton of quantzed hstogram texture features n the DCT doman n Proc. Int. Conf. on Computer and Informaton Scence Kuala Lumpur alaysa vol. Jun. -4 0 pp. 4-40. [8] J. A. Lay and L. Guan Image retreval based on energy hstograms of the low frequency DCT coeffcents n Proc. Int. Conf. on Acoustcs Speech and Sgnal Processng Phoenx AZ USA vol. 6 ar. -9 999 pp. 009-0. [9]. Eom and Y. Choe Fast extracton of edge hstogram n DCT doman based on PEG7 Proceedngs of World Academy of Scence Engneerng and Technology vol. 9 pp. 09- Nov. 00. [0]. Saadatmand-Tarzjan and H. A. oghaddam A novel evolutonary approach for optmzng content-based mage ndexng algorthms IEEE Trans. on Systems an and Cybernetcs - Part B: Cybernetcs Specal Issue on emetc Algorthms vol. 7 no. pp. 9- Feb. 007. [] X. H. Zhang G. C. Ban and W. B. Xu A shape feature based mage retreval n DCT compressed-doman n th IEEE Int. Conf. on Computer and Informaton Technology Shangha Chna Sep. - 00 pp. 69-6. [] J. R. Smth and S. F. Chang Automated bnary texture feature sets for mage retreval n Proc. IEEE Int. Conf. on Acoustcs Speech and Sgnal Processng Atlanta GA USA vol. 4 ay 7-0 996 pp. 9-4. []. G. Albanes and A. Gancane Fast retreval on compressed mages for nternet applcatons n Proc. th Int. Workshop on Computer Archtectures for achne Percepton Padova Italy Sep. - 000 pp. 6-4. [4] H. Fars and S. ohamadzadeh Colour and texture feature-based mage retreval by usng Hadamard matrx n dscrete wavelet transform IET Image Processng vol. 7 no. pp. -8 Apr. 0. []. A. Strcker and. Orengo Smlarty of color mages n Proc. SPIE: Storage and Retreval for Image and Vdeo Databases III San Jose CA USA Feb. 9-0 9 pp. 8-9. [6]. J. Swan and D. H. Ballard Color ndexng Internatonal Journal of Computer Vson vol. 7 no. pp. - Nov. 99. [7]. K. andal T. Aboulnasr and S. Panchanathan Fast wavelet hstogram technques for mage ndexng Computer Vson and Image Understandng vol. 7 no. pp. 99-0 Jul. 999. [8] G. C. Feng and J. Jang Image extracton n DCT doman IEE Proceedngs: Vson Image and Sgnal Processng vol. 0 no. pp. 0-7 Feb. 00. [9] V. Sdorenko C. edna and. Bossert From block to convolutonal codes usng block dstances n Proc. IEEE Int. Symp. on Informaton Theory Nce France Jun. 4-9 007 pp. -. [0] N. V. Patel and I. K. Seth Vdeo shot detecton and characterzaton for vdeo databases Pattern Recognton vol. 0 no. 4 pp. 8-9 Apr. 997. Chengyou Wang was born n Shandong provnce Chna n 979. He receved hs B.E. degree n electronc nformaton scence and technology from Yanta Unversty Chna n 004 and hs.e. and Ph.D. degree n sgnal and nformaton processng from Tanjn Unversty Chna n 007 and 00 respectvely. Now he s an assocate professor n the School of echancal Electrcal and Informaton Engneerng Shandong Unversty Weha Chna. Hs current research nterests nclude dgtal mage/vdeo processng and transmsson technology multdmensonal sgnal and nformaton processng. Xnyue Zhang was born n Shandong provnce Chna n 99. She receved her B.E. degree n communcaton engneerng from Shandong Unversty Weha Chna n 04. Now she s pursung her Ph.D. degree n nformaton and communcaton engneerng n Tsnghua Unversty Chna. Her current research nterests nclude mage retreval and communcaton technology. Rongyang Shan was born n Anhu provnce Chna n 99. He receved hs B.E. degree n communcaton engneerng from Shandong Unversty Weha Chna n 04. Now he s pursung hs.e. degree n sgnal and nformaton processng n Shandong Unversty Chna. Hs current research nterests nclude mage processng and transmsson technques. 0 Engneerng and Technology Publshng 7
Journal of Communcatons Vol. 0 No. January 0 Xao Zhou was born n Shandong provnce Chna n 98. She receved her B.E. degree n automaton from Nanjng Unversty of Posts and Telecommuncatons Chna n 00 her.e. degree n nformaton and communcaton engneerng from Inha Unversty Korea n 00 and her Ph.D. degree n nformaton and communcaton engneerng from Tsnghua Unversty Chna n 0. Now she s a lecturer n the School of echancal Electrcal and Informaton Engneerng Shandong Unversty Weha Chna. Her current research nterests nclude wreless communcaton technology mage processng and transmsson technology. 0 Engneerng and Technology Publshng 7