Patent Application: US-201013496447-A

Abstract:
a system and method are proposed for identifying multimedia files in a first database which are related to a textual term specified by a user . the textual term is used to search a second database of multimedia files , each of which is associated with a portion of text . the “ second database ” is usually composed of files from the databases of a very large number of servers connected via the internet . the multimedia files identified in the search are ones for which the corresponding associated text is relevant to the textual term . the identified multimedia files are used to generate a classifier engine . the classifier engine is then applied to the first database of multimedia files , thereby retrieving multimedia files in the first database which are relevant to the textual term . the user can optionally specify whether the retrieved multimedia files are relevant or not , and this permits a feedback process to improve the classifier engine .

Description:
fig1 shows the steps of an embodiment of the invention to facilitate textual query based retrieval of images in a user &# 39 ; s personal collection . such personal photos ( called here “ target photos ” or “ consumer photos ”) are usually organized in folders without any indexing to facilitate textual queries . fig1 illustrates the steps of a method which is an embodiment of the invention . fig2 illustrates the architecture of a system which performs the method , and shows the flow of information when the method is performed . the system includes a first database 11 which is a collection of the user &# 39 ; s personal photographs , and a second database 12 which is a large collection of images with surrounding texts . the content of the database 12 can be obtained from photosig . com , which is a database described in more detail below , and made up of images originally obtained from the internet , so they are termed “ web images ”. the number of items is so large that almost all daily real - life semantic concepts are represented . we represent these concepts as c w . the database 12 is organized so as to make an image search possible using an “ inverted file method ” [ 31 ]. first , stop - word removal is used to remove from c w high - frequency words that are not meaningful . c w is still very large , and we assume that the set of all concepts c p characterizing a user &# 39 ; s personal collection of images is a subset of c w . in other words , almost all the possible concepts in a personal collection can be expected to be present in the web image database 12 . then , we organize the database 12 as an inverted file , such that it has an entry for each word q in c w , followed by a list of all the images in database 12 that contain the word q in the surrounding texts . the processor which performs the method consists of several machine learning modules . the first module of this framework is a module 13 for automatic web image retrieval . the module 13 receives from the user a query in the form of at least one textual term ( step 1 of the method of fig1 ). in the following description it is assumed that there is only a single textual term defining a single concept , but the method can be generalized straightforwardly to the case of multiple textual terms . the module 13 uses the textual term to extract relevant images from the database 12 ( step 2 ). for any textual term q , the module 13 efficiently retrieves all web images whose surrounding texts contain the word q by using the pre - constructed inverted file . these web images are deemed to be relevant images . in step 3 of the method , the module 13 uses the function wordnet ( a lexical database of the english language maintained by princeton university , and accessible at the website www . wordnet . princeton . edu ) to interpret [ 11 , 22 ] the semantic concept of the textual term ( s ). as illustrated in fig3 , wordnet generates a set c s of “ descendant ” texts of q , based on a specified number of levels in the database . in the example of fig3 , q is “ boat ”, the first - level descendants are “ ark ” and “ barge ”. “ barge ” has two second - level descendants : “ dredger ” and “ houseboat ”. in step 4 , the method retrieves all images in the second database 12 that do not contain any of the words c s in their surrounding texts . these are designated ‘ irrelevant ” web images . where n w is the total number of images in the second database 12 , x i w is the i - th web image , and y i w ε {± 1 } according to whether the i - th image is relevant ( y i w = 1 ) or irrelevant ( y i w =− 1 ). in step 5 , a second module 14 of the system uses these annotated web images as the training set for building a first multimedia file classifier engine ( here called simply a classifier ). in step 6 , the classifier is used for classifying images in the first database 11 ( the target photos ). any classifiers ( such as a k - nearest - neighbor classifier , a decision stump classifier , a support vector machine ( svm ) or a boosting classifier ) can be used in step 5 . however , since the number of web images in d w can be up to millions , direct training of complex classifiers ( such as non - linear svms or boosting classifiers ) may not be suitable for real - time target photo retrieval . the module 14 of fig2 therefore typically uses simple but effective classifiers , such a k - nearest - neighbor classifiers , decision stump ensembles or linear svms . let us first take the case that step 5 constructs a k - nearest - neighbors classifier . in step 6 , the classifier of the module 14 computes , for each of the target photos in the database 11 , the average distance between that target photo and its k nearest neighbors ( knn ) among the relevant web images in d w . for example , k may be taken as 300 . then , the classifier generated by the module 14 ranks all target photos with respect to the average distances to their k nearest neighbors . note that the knn approach does not employ the irrelevant photos for target photo retrieval . to improve the retrieval performance , we can instead in step 5 construct a decision stump ensemble classifier . however , the number of the irrelevant images ( which is typically in the millions ) in d w may be much larger than the number of relevant images , so the class distribution in d w can be very unbalanced . accordingly , following the method proposed in [ 20 ], the classifier generated by the module 14 randomly selects a specified number of irrelevant web images ( denoted here as the “ negative ” samples ), and combines these with the relevant web images ( the “ positive ” samples ) to construct a smaller training set . the smaller training set is used to train a decision stump classifier . the decision stump classifier employs an ensemble of decision stumps , indexed by d . each decision stump relates to a d - th feature in one of the images , and uses a respective function ƒ d ( x )= h ( s d ( x d − θ d )), where θ d is a real number which acts as a threshold , x d denotes the magnitude of the d - th feature within an image x , and s d ε {± 1 }. h ( x ) may be a sign function ( i . e . h ( x )= 1 if x & gt ; 0 , and h ( x )=− 1 otherwise ), in which case the decision stump gives a discrete output . alternatively , h ( x ) may be selected as the symmetric sigmoid activation function h ( x )=( 1 − exp (− x ))/( 1 + exp (− x )), so that the decision stump has a continuous output . this is the selection which is used in the rest of this document . for each d , the values θ d and s d are chosen so as to separate the positive and negative samples with a minimum training error ε d . θ d can be determined by sorting all the samples according to the d - th feature , and scanning the sorted feature values . the corresponding weight γ d for each stump is set to be proportional to 0 . 5 − ε d , where ε d is the training error rate of the d - th decision stump . γ d is further normalized such that to remove the possible side effect of random sampling of the irrelevant images , the whole procedure is repeated n s times , such as n s = 100 times , using different randomly sampled irrelevant web images each time , to produce a source classifier ƒ s which is the average over the n s sets of negative results . note that the average value is not just ± 1 , but is instead a value which can take any of a large number of values for different images x in the second database 11 , so that a ranking of those images is possible based on the corresponding average value . this sampling strategy is known as asymmetric bagging [ 20 ]. after asymmetric bagging with decision stumps , the first multimedia file classifier engine includes n s n d decision stump classifiers , where n d is the feature dimension ( i . e . the number of features ). we may improve the first multimedia file classifier engine by removing a certain proportion ( e . g . 20 %) of the decision stumps with the largest training error rates . this removal process generally preserves the most discriminant decision stumps , and at the same time accelerates the initial photo retrieval process . while the decision stump ensemble classifier can effectively exploit both relevant and irrelevant web photos in d w , this classifier is not optimally efficient for a large consumer photo dataset in database 11 because all the decision stumps need to be applied on every test photo in step 6 ( explained below ). suppose we train n s n d decision stump classifiers , where n d is the feature dimension ( i . e . the number of features ). then , for each test image , all the decision stumps need to be applied in step 6 , which means that even if 20 % of the decision stump classifiers with the largest training rate errors are removed , the floating value comparison and the calculation of exponential function in the symmetric sigmoid activation function will be performed 0 . 8n s n d times . moreover , one decision stump classifier only accounts for one single dimension of the whole feature space . thus , each individual classifier may have very little effect on the final result . to facilitate large scale consumer photo retrieval , yet a further possible implementation of step 5 , is asymmetric bagging with a linear svm . the linear svm classifier is based on loosely labeled web images . once again , we also construct a number of smaller training sets , each of which combines the positive web images with a corresponding randomly - sampled set of the negative web images . as suggested in [ 13 ] feature vectors are normalized into unit hyper - spheres in the kernel space . for linear svm , normalization in kernel space is equivalent to normalization in input space . for each of the sets of negative web images , which are labeled by respective values of the integer variable m , the embodiment uses a respective decision classifier in the form of a linear function ƒ svm ( x )= w ′ m x + b m , where x is a presentation of the image in the feature space ( i . e . it has n d components , each equal to the amount of a corresponding d - th feature in image ), so that the linear svm classifier operates in feature space too , though it does not , like the decision stump classifier , handle each feature dimension independently . the linear svm classifier is trained by minimizing the following objective functional : where { ξ i m } are a set of slack variables and c svm is a tradeoff parameter . the minimization is over the variables w m , b m , and { ξ i m }. c svm , is a predefined parameter , which for example takes the default value 1 in the liblinear toolbox [ 10 ]. we also repeat the whole procedure n s times , each time using a different randomly selected set of irrelevant web images . we then construct the weighted average where γ m ∝ 0 . 5 − ε s , and ε s is the training error rate of the s - th linear svm classifier , and h ( x ) is the symmetric sigmoid activation function defined above . γ m is normalized such that let us now compare the last two ways of implementing step 5 ( i . e . using the ensemble of n s n d decision stumps , or using the linear svm of eqn . ( 3 )). for the same value of n s , it takes more time to train the linear svm classifier than a decision stump ensemble classifier . however , the implementation of step 6 below is much faster with linear svm , since for item in database 11 , the calculation of the exponential function in ( 3 ) only has to be performed n s times and it is unnecessary to perform a floating value comparison . moreover , in the experiments described below , we observe that the linear svm usually achieves comparable or even better retrieval performances , possibly because it simultaneously considers multiple feature dimensions . therefore , linear svm may be preferred for large - scale consumer photo retrieval . the result of step 6 of the method is that the images of the first database 1 are classified based on the textual term . optionally , the method might stop there . however , the user also has the option of improving the classification . if he takes this option in step 7 , then in step 8 the user provides data annotating certain images in the first database 11 to indicate whether they are relevant to the textual term . this is called relevance feedback ( rf ). then in step 9 , a module 15 generates an updated classifier engine , and step 6 is then repeated . this loop may be performed as often as desired , until the user decides in step 7 that no further refinement is needed . note that module 15 may use both the labeled web images created in steps 2 and 4 , and the labeled target photos created in step 8 . we denote the dataset which is the labeled target photos by where n l is the number of labeled target photos , which are indexed by index j . the unlabeled web images together constitute a dataset where n u is the number of unlabeled images . we denote the total dataset from the source domain ( i . e . database 12 ) by d w , and we denote the total dataset from the target domain by d t = d l t ∪ d u t with n t = n l + n u being the total number of images in the target domain . note that the feature distributions of photos from the two different domains ( web images and target photos respectively ) may differ tremendously and thus have very different statistical properties in terms of mean , intra - class and inter - class variance . to utilize all training data from both target photos ( target domain ) and web images ( source domain ) for image retrieval , one can apply known cross - domain learning methods [ 32 , 33 , 6 , 4 , 15 , 7 , 8 ], which we summarize in the rest of this paragraph . yang et al . [ 33 ] proposed a classifier called an “ adaptive support vector machine ” ( a - svm ). the a - svm classifier ƒ t ( x ) is adapted from an existing auxiliary svm classier ƒ s ( x ) trained with the data from the source domain . specifically , the new decision function is formulated as : where the perturbation function δƒ ( x ) is learned using the labeled data d l t from the target domain . as shown in [ 33 ], the perturbation function can be learned by solving a quadratic programming ( qp ) problem which is similar to that used to produce an svm . besides a - svm , many existing works on cross - domain learning attempted to learn a new representation that can bridge the source domain and the target domain . jiang et al . [ 15 ] proposed a classifier called a “ cross - domain svm ” ( cd - svm ), which uses k - nearest neighbors from the target domain to define a weight for each of the web images in the database , and then the svm classifier is trained with re - weighted samples . dauné iii [ 6 ] proposed the “ feature augmentation method ” to augment features for domain adaptation . the augmented features are used to construct a kernel function for kernel methods . note that most cross - domain learning methods [ 32 , 33 , 6 , 15 ] do not consider the use of unlabeled data in the target domain . recently , duan et al . proposed a cross - domain kernel - learning method , referred to as a “ domain transfer svm ” ( dtsvm ) [ 7 ], and a multiple - source domain adaptation method , referred to as a “ domain adaptation machine ” ( dam ) [ 8 ]. however , these methods are either variants of svm , or are used in tandem with svm or other kernel methods . therefore , these methods may not be efficient enough for large - scale retrieval applications . reverting back to the description of the embodiment , a brute - force technique to improve photo retrieval performance would be to combine the web images and the annotated target photos in step 9 to retrain a new classifier . however , since the feature distributions of photos from different domains are drastically different , such classifiers may perform poorly . moreover , it is also inefficient to re - train the classifier using the data from both domains . to significantly reduce the training time , the classifier ƒ s ( x ) ( i . e . the decision stump ensemble classifier given by eqn . ( 1 ), or the linear svm classifier given by eqn . ( 3 )) generated in step 5 can be reused as the “ auxiliary classifier ” for relevance feedback . in a first possibility , step 9 uses a simple cross - domain learning method , referred to here as cdcc or ds_s + svm_t . this method simply combines the weighted ensembles of the decision stumps learned in step 5 from the labeled data in the source domain d w ( referred to as ds_s ), and a svm classifier learned from the much smaller amount of labeled data in the target domain d l t ( a non - linear svm with an rbf ( radial basis function ) kernel , referred to as svm_t ). specifically , the output of svm_t is also converted into the range [− 1 , 1 ] by using the symmetric sigmoid activation function , and then the outputs of ds_s and svm_t are combined with equal weights . as an alternative to ds_s + svm_t , step 9 may use a technique referred to here as “ cross - domain regularized regression ” ( cdrr ). in the sequel , the transpose of a vector or matrix is denoted by the superscript ′. for the j - th sample x j , we use ƒ j t to denote ƒ t ( x j ), and use ƒ j s to denote ƒ s ( x ) where ƒ t ( x ) is the target classifier produced in step 9 , and ƒ s ( x ) is the pre - learnt auxiliary classifier . let us define ƒ l t as [ ƒ 1 t , . . . ƒ n l t ]′, and define y l t as [ y 1 t , . . . t n l t ]′. then we can write the empirical risk as functional of the target decision function on the labeled data in the target domain as : for the unlabeled target patterns d y t in the target domain , let us define the decision values from the target classifier and the auxiliary classifier as f u t =[ ƒ n l + 1 t , . . . ƒ n t t ]′ and f u s =[ ƒ n l + 1 s , . . . , ƒ n t s ]′ respectively . we further assume that the target classifier ƒ t ( x ) should have similar decision values to the pre - computed auxiliary classifier ƒ s ( x ) [ 8 ]. the module 15 uses a regularization term to enforce that the label predictions of the target decision function ƒ t ( x ) on the unlabeled data d u t in the target domain should be similar to the label predictions by the auxiliary classifier ƒ s ( x ). that is , the module 15 simultaneously minimizes the empirical risk of labeled patterns in ( 3 ) and the penalty term in ( 4 ). it does this by minimizing : with respect a set of tunable weight parameters which define the function ƒ t ( x ). here ω ( ƒ t ) denotes a function of the weight parameters which acts as a regularizer to control the complexity of the target classifier ƒ t ( x ). the second term is the prediction error of the target classifier ƒ t ( x ) on the target labeled patterns d l t , and the last term controls the agreement between the target classifier and the auxiliary classifier on the unlabeled samples in d u t , and c & gt ; 0 and λ & gt ; 0 are the tradeoff parameters for the above three terms . in one example , the module 15 uses a target decision function which is a linear regression function , i . e . ƒ t ( x )= w ′ x which is a function of a set of weight parameters w . the regularizer function ω ( ƒ t ) is given by ½ ∥ w ∥ 2 . the structural risk functional ( 5 ) can be solved efficiently by a linear system , by solving the equation : where x l ≡└ x 1 t , . . . x n l t ┘ and x u =[ x n l + 1 t , . . . x n t t ] are data matrices of labeled and unlabeled target photos respectively , and i is the identify matrix . this has the closed - form solution : a further alternative to the ds_s + svm_t and cdrr techniques discussed above is that in step 9 the module 15 performs a hybrid method to take the advantages of both ds_s + svm_t and cdrr . after the user marks the target photos in step 8 , the module 7 measures the average distanced d between the labeled positive images and their ρ nearest neighbor target photos ( ρ is set as 30 in the numerical experiments explained below ). we have observed that when d is larger than a threshold ε , ds_s + svm_t is generally better than cdrr ; otherwise , cdrr generally outperforms ds_s + svm_t . the module 15 therefore uses a hybrid approach to perform step 9 as illustrated in fig4 . in sub - step 9 a , the module 15 calculates ρ , in sub - step 9 b the module 15 determines if ρ is above or below ε , and accordingly it performs relevance feedback to construct the target classifier either using ds_s + svm_t ( sub - step 9 c ) or cdrr ( sub - step 9 d ). a yet further alternative is to perform a form of cdrr which employs an incremental updating of the weights each time the feedback loop ( i . e . steps 7 to 9 ) is performed . this possibility is referred to here as icdrr . in our icdrr , we incrementally update the two matrices a 1 = x l x ′ l and a 2 = x u x ′ u , and two vectors b 1 = x l y l t and b 2 = x u f u t in eqn . ( 9 ). let us number the rounds of relevance feedback by the integer variable r , where r = 0 corresponds to the situation before relevance feedback . the realizations of a 1 , a 2 , b 1 and b 2 in the r - th round of relevance feedback are denoted by a 1 ( r ) , a 2 ( r ) , b 1 ( r ) and b 2 ( 2 ) respectively . before relevance feedback we initialize a 1 ( 0 ) = 0 , a 2 ( 0 ) = xx ′, b 1 ( 0 ) = 0 and b 2 ( 0 ) = xf s , where x is the data matrix of all consumer photos in the database 11 . f s is the output of the first multimedia file classifier on all consumer photos . in the r - th round of relevance feedback , we then incrementally update a 1 , a 2 , b 1 and b 2 by : b 1 ( r ) = b 1 ( r - 1 ) +( δ x )( δ y ) ( 12 ) b 2 ( r ) = b 2 ( r - 1 ) −( δ x )( δ f s ). ( 13 ) these equations give exactly the same results as cdrr , not just a very good approximation . in the above equations , δxεr n d × n c , δyεr n c and δf s εr n c are the data matrix , label vector , and the response vector from first multimedia file classifier from the current round , of the newly labeled consumer photos in the current round , where n c is the number of user - labeled consumer photos in this round , and n d is the feature dimension . the total complexity to directly calculate a 1 and a 2 in cdrr is o ( n d 2 n t ), while the total complexity to incrementally update a 1 and a 2 in icdrr is only o ( n d 2 n c ). similarly , the total complexity to directly calculate b 1 and b 2 in cdrr is o ( n d n t ), while the total complexity to incrementally update b 1 and b 2 in icdrr is only o ( n d n c ). the user only labels a very limited number of consumer photos in each round of relevance feedback ( i . e . n c is much smaller than n t ), so the computational cost for updating a 1 ( r ) , a 2 ( r ) , b 1 ( r ) and b 2 ( 2 ) becomes negligible in icdrr . moreover , a 2 ( 0 ) = xx ′ can be computed offline because it does not depend on the first multimedia file classifier , and b 2 ( 0 ) = xf s can be computed when the user inspects the initial retrieval result ( it costs less than 0 . 15 seconds with one single cpu thread even on the nus - wide dataset described below with about 270k images ). therefore in our experiments , we do not count the time for calculating a 2 ( 0 ) = xx ′ and b 2 ( 0 ) = xf s . the experimental results show that icdrr significantly accelerates the relevance feedback process for large scale photo retrieval . 2 . 1 setting up the databases 11 and 12 of the embodiment we have evaluated the performance of the embodiment for textual query based target photo retrieval . first , we compared the retrieval performances using in step 5 either the knn classier based method , the decision stump classier based method , and linear svm classifier without using relevance feedback . second , we evaluated the effect of relevance feedback using methods ds_s + svm_t and cdrr . the second database 12 was formed using about 1 . 3 million photos from the photo forum photosig as the training dataset . most of the images are accompanied by rich surrounding textual descriptions ( e . g ., title , category and description ). after removing the high - frequency words that are not meaningful ( e . g ., “ the ”, “ photo ”, “ picture ”), our dictionary contains 21 , 377 words , and each image is associated with about five words on the average . similarly to [ 29 ], we also observed that the images in photosig generally are high resolution with the sizes varying from 300 × 200 to 800 × 600 pixels . in addition , the surrounding descriptions more or less describe the semantics of the corresponding images . we tested the performance of the embodiment using two successive databases as the first database 11 . the first test dataset (“ the kodak dataset ) was derived from the kodak consumer video benchmark dataset [ 17 ], which was collected by eastman kodak company from about 100 real users over the period of one year . in this dataset , 5 , 166 key - frames ( the image sizes vary from 320 × 240 to 640 × 480 pixels ) were extracted from 1 , 358 consumer video clips . key - frame based annotation was performed by the students at columbia university to assign binary labels ( presence or absence ) for each visual concept . to the best of our knowledge , this dataset is the largest annotated dataset from personal collections . note that this annotation data was only used in this experiment to evaluate the performance of the embodiment ; it was not used by the embodiment to retrieve photos from the kodak database . twenty - five semantic concepts were defined , including 22 visual concepts and three audio - related concepts ( i . e . “ singing ”, “ music ” and “ cheer ”). we also combined two concepts “ group of two ” and “ group of three or more ” into a single concept (“ people ”) for the convenience of searching for relevant and irrelevant images from the photosig web image dataset . observing that the keyframes from the same video clip are generally near duplicate images , we select only the first keyframe from each video clip in order to fairly compare different algorithms . in total , we tested our framework on 21 visual concepts and with 1 , 358 images . the second test dataset was the corel stock photo dataset [ 27 ]. we recognized that corel is not a target photo collection , but decided to include it nevertheless because it was used in other studies and also represents a cross - domain case . we use the same subset as in [ 9 ], in which 4 , 999 images ( the image sizes are 192 × 128 or 128 × 192 pixels ) are manually annotated in terms of over 370 concepts . since many concepts have very few images , we only chose 43 concepts that contain at least 100 images . the third test database was the nus - wide database [ 5 ], which was collected by the national university of singapore ( nus ). in total , this dataset has 269 , 648 images and ground - truth annotations for 81 concepts . the images in the nus - wide dataset are downloaded from the online consumer photo sharing website flickr . com . we choose nus - wide dataset because it is the largest annotated consumer photo dataset available to researchers today , and is suitable for testing the performances of our framework for large - scale photo retrieval . moreover , it is also meaningful to use this dataset to test the retrieval precisions of our cross - domain relevance feedback methods cdcc and cdrr because the data distributions of photos downloaded from different websites , i . e . photosig . com and flickr . com are still different . it is also worth noting that the images in nus - wide are used as raw photos , in other words , we do not consider the associated tag information in this work . in our experiments , we used three types of global features . for grid color moment ( gcm ), we extracted the first three moments of three channels in the lab color space from each of the 5 × 5 fixed grid partitions , and aggregated the features into a single 225 - dimensional feature vector . the edge direction histogram ( edh ) feature includes 73 dimensions with 72 bins corresponding to edge directions quantized in five angular bins and one bin for non - edge pixels . similarly to [ 5 ], we also extracted 128 - dimensional wavelet texture ( wt ) features by performing a pyramid - structured wavelet transform ( pwt ) and a tree - structured wavelet transform ( twt ). finally , each image was represented as a single 426 - dimensional vector by concatenating three types of global features . refer to [ 5 ] for more details about the features . we use the above global features because they can be efficiently extracted over the large image corpus and they have been shown to be effective for consumer photo annotation in [ 5 ]. for the training dataset photosig , we calculated the original mean value μ d and standard deviation σ d for each dimension d , and also normalized all dimensions to zero mean and unit variance . we also normalized the three test datasets ( i . e . the kodak , corel and nus - wide databases ) by using μ d and σ d . the experiments are performed on a server machine with dual intel xeon 3 . 0 ghz quad - core cpus ( eight threads ) and 16 gb memory . our system is implemented in c ++. matrix and vector operations are performed using the intel math kernel library 10 . 0 . we now describe a first set of experiments performed using the kodak and corel databases . to improve the speed and reduce the memory cost , we first performed principal component analysis ( pca ) using all the images in the photosig dataset . we observed that the first n d = 103 principal components are sufficient to preserve 90 % energy . therefore , all the images in the training and test datasets were projected into the 103 - dimensional space after dimension reduction . considering that the queries in known cbir methods and our framework are different in nature , we cannot compare our work with the existing cbir methods before relevance feedback . we also cannot compare the retrieval performance of our framework with web - based annotation methods , because of the following two aspects : 1 ) the prior works [ 16 , 22 , 23 , 25 , 28 , 29 ] only output binary decisions ( presence or absence ) without providing a metric to rank the personal photos ; 2 ) an initial textual term is required before image annotation in [ 14 , 28 , 29 ] and their annotation performances depend heavily on the correct textual term , making it difficult to fairly compare their methods with our automatic technique . however , we notice that the previous web - based image annotation methods [ 16 , 22 , 23 , 25 , 28 , 29 ] all used a knn classifier for image annotation , possibly owing to its simplicity and effectiveness . therefore , we directly compared the retrieval performance of decision stumps and the base - line knn classifier . suppose a user wants to use the textual query q to retrieve the relevant personal images . for both methods , we randomly select n p positive images ( that is , images for which the surrounding textual descriptions contains term q ) from the photosig dataset where n p is the lesser of 10000 and n q , where n q is the total number of images that contain the word q in the surrounding textual descriptions . the kodak and corel datasets contain 61 distinct concepts in total ( the concepts “ beach ”, “ boat ” and “ people ” appear in both datasets ). the average number of selected positive samples of all the 61 concepts is 3703 . 5 . in the case that the embodiment uses decision stumps , we also randomly choose n p negative samples ( that is , images for which the surrounding textual descriptions do not contain the term q ). this was done n s times , each time using a different set of n p negative samples . n s was set to 100 in the experiment . thus , in total the embodiment trained n s × n d = 10300 decision stumps . as mentioned above , the 20 % of the decision stumps which have the largest training error rates are removed before computing the weighted ensemble output . there are 21 concept names from kodak dataset and 43 concept names of corel dataset , respectively . they are used as textual queries to perform image retrieval . a parameter called “ precision ” is defined as the percentage of relevant images in the top i retrieved images , where i is an integer . the precision parameter is used as the performance measure to evaluate the retrieval performance . since online users are usually interested in the top ranked images only , the experiments used 20 , 30 , 40 , 50 , 60 and 70 as the value of i , similarly to [ 20 ]. for any query q , we rank the consumer photos in kodak and corel databases using the embodiment . we then compare the ranked results and ground - truth labels obtained by manual annotation to calculate the precision . decision stumps based on the training data from the source domain ( referred to as ds_s ) generally outperform knn . this is possibly because ds_s employs both positive and negative samples to train the robust classifier while knn only utilizes the positive samples . a first experiment was performed in which the textual term q used as a query was “ pool ”. note that this query is not in the concept lexicon of the kodak dataset . in this subsection , we evaluate the performance of the embodiment when incorporating the feedback steps 7 - 9 . for fair comparison , the embodiment used ds_s to obtain the initial retrieval results for all the methods except for the baseline knn based rf method knn_rf and a - svm [ 33 ], which use knn and svm for initial retrieval respectively . for cdrr , the value of c was empirically chosen to be 20 . 0 . a was set to 0 . 02 for the first feedback round , and to 0 . 04 for the remaining rounds . it was observed that cdrr generally achieves better performance if we respectively set y j t = 1 for positive images , and y j t =− 0 . 1 for negative images , rather than setting y j t = 1 for positive images and y j t =− 1 for negative images as described above . it is better to set y j t =− 0 . 1 for the negative images because , whereas the positive images marked by the user are mainly top - ranked images , the negative images marked by the user in the relevance feed - back are typically not extremely negative images . in our hybrid method , we empirically fixed ρ to be 30 , and set ε to be 14 . 0 and 10 . 8 for kodak and corel datasets respectively . we compared the ds_s + svm_t classifier , the cdrr classifier and the hybrid method with the following methods : 1 ) knn_rf : the initial retrieval results are obtained by using knn . in each feedback round , knn is performed again on the enlarged training set , which includes the labeled positive feedback images marked by the user in the current and all previous rounds , as well as the original n p positive samples from the photosig dataset obtained before relevance feedback . the rank of each test image is determined based on the average distance to the top - 300 nearest neighbors from the enlarged training set . 2 ) svm_t : a svm has been used for rf in several existing cbir methods [ 20 , 21 , 34 ]. we trained a svm based on the labeled images in the target domain , which are marked by the user in the current and all previous rounds . we set c = 1 and γ in the rbf kernel to be 1 / 103 . 3 ) a - svm : adaptive svm ( a - svm ) is a recently proposed method [ 33 ] for cross - domain learning as described above . a svm based on rbf kernel is used to obtain the initial retrieval results . the parameter setting is the same as that in svm_t . 4 ) mr : manifold ranking ( mr ) is a semi - supervised rf method proposed in [ 12 ]. the parameters α and γ for this method are set according to [ 12 ]. in real circumstances , the users typically would be reluctant to perform many rounds of relevance feedback or annotate many images for each round . therefore , we only report the results from the first four rounds of feedback . in each feedback round , the user marks one or more relevant images ( these can be any of the images , but typically user prefer to mark the highest ranked images ) out of the top 40 images as a positive feedback sample . similarly , one or more negative samples out of the top 40 images are marked . 1 ) when the embodiment uses the cdrr and ds_s + svm_t methods for rf , it outperforms the rf methods knn_rf , svm_t and mr as well as the existing cross - domain learning method a - svm in most cases , because they successfully utilize the images from both domains . by taking the advantages of ds_s + svm_t and cdrr , the hybrid method generally achieves the best results . when comparing the hybrid approach with svm_t after the first round of relevance feedback , the relative improvements are no less than 18 . 2 % and 19 . 2 % on the corel and kodak datasets , respectively . moreover , the retrieval performance of our cdrr , ds_s + svm_t and the hybrid method increase monotonically with more labeled images provided by the user in most cases . for cdrr , we believe that the retrieval performance can be further improved by using a non - linear function in cdrr . however , it is a non - trivial task to achieve the real - time retrieval performance with rbf kernel function . 2 ) the retrieval performances of knn_rf are almost the same , even after 4 rounds of feedback , possibly because the limited number of user - labeled images in the target domain cannot influence the average distance from the nearest neighbors , and because of the knn method &# 39 ; s inability to utilize negative feedbacks ; 3 ) for svm_t , the retrieval performances sometimes drop after the first round of rf , but increase from the second iteration . the explanation is that since svm_t is trained using a limited number of labeled training images , it is not reliable , but its performance can improve when more labeled images are marked by the user in the subsequent feedback iterations . 4 ) the performance of a - svm is slightly improved after using rf in most cases . it seems that the limited number of labeled target images from the user are not sufficient to facilitate robust adaptation for a - svm . the initial results of a - svm were better than dss on the kodak dataset because of the utilization of svm for initialization . however , it takes more than 10 minutes to train the svm classifier , making it unsuitable for the practical image retrieval application . 5 ) the semi - supervised learning method mr can improve the retrieval performance only in some cases on kodak dataset , possibly because the manifold assumption does not hold well for unconstrained consumer images . the running times of the embodiment for the initial retrieval and rf are shown table 1 and table 2 , respectively . in this work , each decision stump classifier can be trained and used independently . therefore , we also use the simple but effective parallelization scheme , openmp , to take advantages of multiple threads . in table 1 and 2 , we do not consider the time of loading the data from the hard disk because the data can be loaded once and then used for subsequent queries . the times given in the tables are average cpu times in seconds . in table 2 , the times are given for one round of rf with one single thread . as shown in table 1 , for the ds_s the average running time of the initial retrieval for all the concepts is about 8 . 5 seconds with a single thread and 2 seconds with 8 threads . as can be seen from table 2 , the rf process of ds_s + svm_t and cdrr is very responsive , because module 15 only needs to train a svm with less than 10 training samples for ds_s + svm_t , or solve a linear system for cdrr ( using eqn . ( 7 )). in practice , ds_s + svm_t , cdrr and the hybrid method all take less than 0 . 1 seconds per round . therefore , our system is able to achieve real - time retrieval . all the other methods , except for a - svm , can also achieve real - time retrieval . similarly to [ 33 ], we train an svm classifier based on an rbf kernel to obtain the initial retrieval result for a - svm . while the initial retrieval performance of a - svm is better than ds s on the kodak dataset , it takes 610 . 9 s . in the relevance feedback stage , the target classifier is adapted from the initial svm classifier . its speed is also very slow ( about 26 seconds per round ), making it infeasible for interactive photo retrieval . in conclusion , the embodiment when using the simple decision stump classifier as the source classifier achieved ( quasi ) real - time response . the hybrid method in particular requires an extremely limited amount of feedback from the user and it outperforms other popular relevance feedback methods . some efficient linear svm implementations ( e . g ., liblinear ) may be also used in the embodiment . in addition , non - linear functions may be also employed in cdrr to further improve the performance of the embodiment . in these experiments we directly compared the retrieval performance of decision stump ensemble classifier and the linear svm classifier , using as a baseline the k - nn classifier . again , suppose a user wants to use the textual query q to retrieve the relevant personal images . for each classifier , we randomly select n p positive images ( that is , images for which the surrounding textual descriptions contains term q ) from the photosig dataset where n p is the lesser of 10000 and n q , where n q is the total number of images that contain the word q in the surrounding textual descriptions . the kodak and nus - wide datasets contain 94 distinct concepts in total ( the concepts “ animal ”, “ beach ”, “ boat ”, “ dancing ”, “ person ”, “ sports ”, “ sunset ” and “ wedding ” appear in both datasets ). the average number of selected positive samples of all the 94 concepts is 3088 . 3 . to improve the speed and reduce the memory cost , we perform principal component analysis ( pca ) using all the images in the photosig dataset . we also compare the performances of two possible fusion methods to fuse three types of global features in this application . early fusion : we concatenate the three types of features before performing pca . we observe that the first n d = 103 principal components are sufficient to preserve 90 % of the energy . after dimension reduction , all the images in training and test datasets are projected into the 103 - d space for further processing . late fusion : we perform pca on three types of features independently . we observe that the first n d1 = 91 , n d2 = 24 , n d3 — = 5 principal components are sufficient to preserve 90 % of the energy for gcm , edh and wt features , respectively . then , these three types of features of all the images in the training and test datasets are projected to n d1 - d , n d2 - d , n d3 - d space respectively after dimension reduction . we train independent classifiers based on each type of feature . finally , the classifiers from different features are linearly combined with the combination weights determined based on the training error rates . k - nn_s : we only use the positive images from the web - image database as the training data . for each consumer photo from the testing dataset , we find the top k nearest neighbors in the positive images , and use the average distance to measure the relevance between the textual query and the testing consumer photo . in the experiment , we set k = 200 . we also perform an exhaustive exact k - nn search accelerated by simd cpu instructions and multiple threads . for the k - nn based method with late fusion , we combine the outputs of all k - nn classifiers with equal weights because the training error rate of the k - nn classifier on each type of feature is unknown in this case . in the sequel , we denote k - nn_s with early fusion and late fusion by k - nn_se and k - nn_sl , respectively . ds_s : we randomly choose n p negative samples n s times , and in total we trained n s n d decision stumps for early fusion ( referred to as ds_se ) or n s ( n d1 + n d2 + n d3 ) for late fusion ( referred to as ds_sl ). after removing the 20 % of the decision stumps with the largest training error rates , we apply 0 . 8n s n d or 0 . 8n s ( n d1 + n d2 + n d3 ) decision stumps for the testing stage in ds_se and ds_sl , respectively . linsvm_s : we also randomly choose n p negative samples n s times . in total , we trained n s linear svm classifiers for early fusion ( referred to as linsvm_se ) or 3n s classifiers for late fusion ( referred to as linsvm_sl ). in this work , we use tools from liblinear [ 10 ] in our implementations and use the default value 1 for the parameter c svm . there are 21 and 81 concept names from the kodak dataset and nus - wide dataset , respectively . they are used as textual queries to perform image retrieval . precision ( defined as the percentage of relevant images in the top i retrieved images ) is used as the performance measure to evaluate the retrieval performance . since online users are usually interested in the top ranked images only , we set i as 20 , 30 , 40 , 50 , 60 and 70 . we tested all the methods above for initial retrieval without using relevance feedback . for the kodak dataset , we set the number n s of sets of random samples of negative images to 50 for ds_se and ds_sl , and 10 for linsvm_se and linsvm_sl in order to make the running time of initial retrieval process under 1 second . the precisions of all methods are shown in fig5 . we observe that ds_se , ds_sl , linsvm_se and linsvm_sl are much better than k - nn_se and k - nn_sl . this is possibly because k - nn_se and k - nn_sl only utilize the positive web images while the other methods take advantage of both the positive and negative web images to train the more robust classifiers . moreover , the average values of the top 20 , 30 , 40 , 50 , 60 and 70 precisions from linsvm_sl , ds_sl , linsvm_se and ds_se , are 14 . 50 %, 14 . 47 %, 14 . 39 % and 14 . 21 %, respectively . we concluded that the linear svm classifier and decision stump ensemble classifier achieved comparable retrieval performances on the kodak dataset . to better compare the performances of different algorithms , we also tested them on the large nus - wide dataset , and found precision variations of the different algorithms with respect to different values of n s , in which n s is set to 1 , 3 , 5 , 7 and 10 . we made the following observations : 1 ) again , k - nn_se and k - nn_sl achieve much worse performances , when compared with the other four algorithms . linsvm_sl generally achieves the best results and it is slightly better than ds_sl in most cases . 2 ) when n s increases , ds_se , ds_sl , linsvm_se , and linsvm_sl improve in most cases , which is consistent with the recent work [ 20 ]. 3 ) it is interesting to observe that linsvm_se is the worst among the four algorithms related to linear svm and decision stump ensemble classifiers . we employed three types of features ( color , edge and texture ), and it is well known that none of them can work well for all concepts . linsvm_sl , ds_sl and ds_se achieve better performance , possibly because they can fuse and select different type of features or even feature dimensions based on the training error rates . 4 ) except for k - nn classifier based algorithms , we also observed that the late fusion based methods are generally better than the corresponding early fusion based methods for photo retrieval on the nus - wide dataset . k - nn_sl is worse than k - nn_se . however , in k - nn_sl , all types of features are combined with equal weights , namely , feature selection is not performed in k - nn_sl . the embodiment used the keyword “ water ” to retrieve images from the kodak dataset using linsvm_sl with 10 svm classifiers . note that this query is undefined in the concept lexicon of the kodak dataset . fig6 shows the top 10 images : that is , the 10 images ranked most highly . all but the 2nd and 6 - th results are relevant images . these irrelevant images are highlighted in the figure . in another experiment , the embodiment used the keyword “ animal ” to retrieve images from the nus - wide database using linsvm_sl with 10 svm classifiers (“ animal ” is defined in the concept lexicon of nus - wide ). the embodiment produces six relevant images out of the top 10 retrieved images . we also compared the running time of all algorithms on the two datasets . each decision stump classifier and svm classifier can be trained and used independently , and exhaustive k - nn search is also easy to parallelize . we therefore use a simple but effective parallelization scheme , openmp , to take advantages of eight threads of our server for each method . the average , minimum and maximum running time in the initial retrieval process on the kodak and nus - wide dataset are reported in table 1 . for ds_se and ds_sl , n s = 50 is used on the kodak dataset and n s = 10 is used on the nus - wide dataset . for linsvm_se and linsvm_sl , n s = 10 is used on both datasets . on the average , k - nn_se spends 0 . 872 seconds for the initial retrieval process on the kodak dataset . on the average , k - nn_sl spends 1 . 033 seconds for the initial retrieval process on the kodak dataset . on the average , ds_se and ds_sl with n s = 50 , and linsvm_se and linsvm_sl with n s = 10 , spent 0 . 912 , 0 . 969 , 0 . 830 , and 0 . 852 seconds , respectively . all methods can achieve real - time retrieval performance on this small dataset . on the nus - wide dataset , k - nn_se and k - nn_sl spent 213 . 35 and 225 . 73 seconds , respectively . we implement a k - nn based on exhaustive search , thus it takes much more time when compared with decision stump ensemble classifier and linear svm classifier . when n s is 10 , linsvm_se , linsvm_sl are much faster than ds_se and ds_sl in terms of the minimum cpu time . the average total running time of linsvm_se , linsvm_sl , ds_se and ds_sl are 0 . 782 , 0 . 878 , 1 . 373 and 1 . 575 seconds , respectively . we also observe that linsvm_se and linsvm_sl generally cost more time than ds_se and ds_sl in the training stage . however , the testing stage of linsvm_se and linsvm_sl is much faster , making the average total running time of initial retrieval process much shorter than ds_se and ds_sl . from the experiments on the kodak dataset , we observe that methods based on the linear svm classifier and the decision stump ensemble classifier are generally comparable in terms of initial retrieval precision and speed . since all the algorithms can achieve real - time speed , any of them can be used for initial retrieval on a small dataset . however , for large - scale photo retrieval , linsvm_sl is preferred for the initial retrieval process because of its effectiveness and real - time response . in this section we evaluate the performance of a few relevance feedback methods . for fair comparison , we choose linsvm_sl with 10 svm classifiers , which as demonstrated above was the best algorithm in terms of overall performances for retrieval before relevance feedback . linsvm_sl is also accordingly chosen as the source classifier in our methods cdcc and cdrr . from here on , we also refer to cdcc as linsvm_sl + svm_t , in which the responses from linsvm_sl and svm_t are equally combined . in our linsvm_sl + svm_t , cdrr and two conventional manifold ranking and svm based relevance feedback algorithms [ 12 , 34 ], we also adopt the late fusion scheme used in linsvm_sl to integrate the three types of global features , namely , the three types of features are used independently at first and the decisions or responses are finally fused . the early fusion approach is used for the prior cross - domain learning method a - svm [ 33 ] because it is faster . we compare our linsvm_sl + svm_t method and cdrr with the following methods : 1 ) svm_t : svm has been used for rf in several existing cbir methods [ 20 , 21 , 34 ]. we train a non - linear svm with an rbf kernel based on the labeled images in the target domain , which are marked by the user in the current and all previous rounds . we use libsvm package [ 2 ] in our implementation and use its default setting for rbf kernel ( i . e . c is set as 1 and y in the rbf kernel is set as 1 / 91 , 1 / 24 and ⅕ for gcm , edh and wt features , respectively ). 2 ) mr : manifold ranking ( mr ) is a semi - supervised rf method proposed in [ 12 ]. the two parameters α and γ for this method are set according to [ 12 ]. 3 ) a - svm : adaptive svm ( a - svm ) is a recently proposed method [ 33 ] for cross - domain learning , in which svm based on an rbf kernel is used as the source classifier to obtain the initial retrieval results . the parameter setting is the same as that in svm_t . considering the running time of a - svm is much higher than other methods even on the small kodak dataset , we do not test it on the large nus - wide dataset because it cannot achieve real time response . as in other methods [ 12 , 33 , 34 ], several parameters needed to be decided beforehand . in linsvm_sl + svm_t , we need to determine the parameters in svm_t and we use the same parameters setting as that in svm_t . for cdrr , we empirically fix c = 70 . 0 and set λ = 0 . 05 on the kodak dataset and λ = 0 . 02 on the nus - wide dataset . we use a smaller λ on the nus - wide dataset in order to avoid over - emphasizing the labeled data in the large photo dataset . in addition , as in the experiments reported earlier , we also observe that cdrr generally achieves better performance , if we respectively set y i t = 1 and y i t =− 0 . 1 for positive and negative consumer photos , when compared with the setting y i t = 1 and y i t =− 1 , so we set y i t =− 0 . 1 for negative images . as in the earlier experiment , we only report the results from the first four rounds of feedback . in each feedback round , the user marks the top ranked relevant image out of the top 40 images as a positive feedback sample . similarly , one negative sample out of the top 40 images is marked . the embodiment was then used to perform one round of relevance feedback for the query “ animal ” on the nus - wide dataset . fig7 ( a ) shows the result of an experiment of running the embodiment without using relevance feedback , using the concept “ animal ”. out of the 10 top images , four are incorrect ( the 2 nd , 5 th , 7 th and 8 th images ). fig7 ( b ) shows the top - 10 retrieved images after one round of relevance feedback for the same query . only the 7 - th image is now incorrect . we observe that the results are improved considerably after using the cdrr relevance feedback algorithm . from these results , we have the following observations : 1 ) the cdrr and linsvm_sl + svm_t algorithms outperform the conventional rf methods svm_t and mr , because of the successful utilization of the images from both domains . when comparing cdrr with svm_t and mr , the relative precision improvements after rf are more than 14 . 7 % and 13 . 5 % on the kodak and nus - wide datasets , respectively . cdrr is generally better than or comparable with linsvm_sl + svm_t , and the retrieval performances of our cdrr and linsvm_sl + svm_t increase monotonically with more labeled images provided by the user in most cases . 2 ) for svm_t , the retrieval performance drops after the first round of rf , but increases from the second iteration . the explanation is that svm_t trained based on two labeled training images is not reliable , but its performance can improve when more labeled images are marked by the user in the subsequent feedback iterations . 3 ) semi - supervised learning method mr can improve the retrieval performance only in some cases on the kodak dataset , possibly because the manifold assumption does not hold well for unconstrained consumer images . 4 ) the performance of a - svm is slightly improved after using rf in most cases . it seems that the limited number of labeled target images from the user are not sufficient to facilitate robust adaptation for a - svm . we also observe that initial results of a - svm is better than other algorithms on the kodak dataset because of the utilization of non - linear svm for initialization . however , it takes 324 . 3 seconds with one thread for the initial retrieval process even on the small - scale kodak dataset , making it unsuitable for practical image retrieval applications even with eight threads . we now compare the running time of all relevance feedback algorithms used in our experiment . considering that all the algorithms except a - svm and mr on the nus - wide dataset are very responsive , we test all the algorithms by using only one single thread for relevance feedback . the comparison of time cost on the kodak dataset is shown in table 2 . all methods except a - svm are able to achieve the interactive speed on this small dataset . in addition , the incremental cross - domain learning method icdrr is faster than cdrr . in table 3 , we report the running time of different algorithms on the nus - wide dataset . mr is no longer responsive in this case because the label propagation process based on the graph with much more vertices becomes much slower . the rf process of cdrr and linsvm_sl + svm_t ( or svm_t ) is still responsive ( on the average 1 . 534 seconds and 1 . 277 seconds only ), because we only need to train svm with less than 10 training samples for linsvm_sl + svm_t and svm_t or solve a linear system for cdrr . moreover , icdrr only takes about 0 . 1 seconds on the average per round after incrementally updating the corresponding matrices , which is much faster than cdrr . we also observe that the running time of linsvm_sl + svm_t ( or svm_t ) increases when the number of user - labeled consumer photos increases in the subsequent iterations . specifically , when the user labels 1 , 2 , 3 , 4 positive consumer photos and the same number of negative photos , linsvm_sl + svm_t ( or svm_t ) costs about 0 . 7 , 1 . 1 , 1 . 5 and 1 . 9 seconds on average , respectively . however , icdrr takes about 0 . 1 seconds on the average in all the iterations . in short , icdrr can learn the same projection vector w and achieve the same retrieval precisions as cdrr , but it is much more efficient than cdrr and linsvm_sl + svm_t for relevance feedback in large scale photo retrieval . the disclosure of the following citations is incorporated herein by reference : l . cao , j . luo , and t . s . huang . annotating photo collections by label propagation according to multiple similarity cues . in acm mm , 2008 . 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