okay are are about the non here one my name's is on you and uh i'm from national institute of informatics uh in japan so today a uh this talk is on our recent work but i know use a temporal recurrence hashing our reason uh for mining commercials from a to a string so come from mining is an important uh preprocessing task you know you though uh a cost "'em" at i an can and uh uh market research it i'm i'm at detecting and localising a duplicate commercial sequences from a large scale uh a video archive or to scheme and a one month us archive there could be chains oh of uh thousands of uh a duplicate uh commercial sequences so many uh detecting and the localising these so many uh commercial sequences is too time consuming and uh uh labour intensive so an automatic a commercial mining technique is needed so one direction of commercial mining is known in is not each based uh commercial mining it use it the uh the intrinsic uh characteristics of commercials forty as a up to an eight for detecting on for instance a a use some can trees the T V stations may uh at some one a monochrome or silence frames uh into that under speech into neighbouring commercial segments so if we can detect the position of these frames then we can use it to get the uh duration uh i mean the location of the commercial sequence so most and no each based a techniques are uh efficient but it's not generate enough because of that data dependent uh a knowledge they use uh because these up real knowledge uh maybe be barry this can trees and time i where there is one up to an order that can be used for detecting commercial but to view uh not to be scanned all times that is commercials are reputations so this kept to just take you never change and uh inspired the another uh direction known as reputation based commercial mining so most uh uh reputation based techniques are and super wide generate more generate but uh a lot uh can board so in this study we proposed a simple but very effective a call reason uh for for the and supervised uh generate each and out high speed commercial mining so in this study there is no uh training that he's not harry provide a before and what do we have a is only a very wrong uh to tree and uh the a priest does not depend on any prior knowledge that my they have can't result time and uh also the are rings is very fast uh for ten hour stream uh the person time was only a four seconds and for one man a the time was this simple forty two minutes and for for five here we do stream the processing time was this some twenty one hours it's very far so the proposed uh uh we're is is a a two-stage hashing out and uh i i really should i we explain the a two state one by one and before explaining the first stage by like to discuss that difference between commercials and and new duplicate video and a it's you mean know a by definition your to decades a carriers that's all approach to to D uh identical videos it's videos are normally are derived from an original deal by means of various is transformation such as uh and coding your train picture and something that and on the other hand commercials ah exact duplicates derived from the original video deal on any transformation so commercials can be considered as a special case of near to the kate we'd so this is the main difference between commercials and near to K and in the case of commercials uh the fragments for example the frames all the shops sub shots oh the but videos can be translated into uh a a be every compact uh in so that identical fragments across that duplicates can be mapped to exactly the same thing to for so based on this assumption if we insert the fragments i into a hash table by regarding the fingerprint as a you but in in X uh a a hash collision we occur in the corresponding has pocket so uh the duplicate uh fragments can be easily detect it by based on a uh could each a tech process so these assumptions uh uh not to reasonable for in the case of near to as but only reasonable for exact duplicates like commercial so in this study we propose applying a luminance based fingerprint stage she to the be do stream and also a light uh and use it all your hashing technique to the audio stream and we didn't test all the the you existing techniques but to be believe that the the proposed our reason is performance uh you by rent to with a fingerprint sticky so any existing one can you so we apply these two fingerprinting straight is two all frames of the but and uh so here the same color indicates uh frames uh with the same fingerprint and uh the canteen hours frames with the same finger uh a a and boat into a fraction so is a huge hole based on the the condition shouldn't the hash collision uh a tech that's we can use the to detect duplicate fragments but please note that the goal of commercial mining is not to detect these do you keep experiments but to detect do P eight see a uh the commercial sequence is normally composed of uh site chains all few hundred all a fragment so here we read got uh that duplicate fragment parents that the basic unit and a project them to of the time X uh from this figure we can also so strong temporal consistency uh among these pairs for instance the uh positions of the fragments of consecutive and of the temporal interval each in each to fragment almost the same so this kind of temporal consistency is very useful for distinguishing duplicate sequences from non duplicate one and the the time more time the commercial mining a task can be formulated into a searching for duplicate fragment carrots with high temporal consistent so one sort of and to this is to a i a a pairwise matching based on temporal information to all pairs scope duplicate right so people and P do you know the number of the the confusion cost is in your to the scale and P and we can see and P stomp be a very very large number and uh a little or uh can can in cost can be obtained by of lighting pair was making based on temporal information to all uh all sets of duplicate fragments uh so give a and all you noting the that's oh this this actually and all use than then all the uh a the are of beans in the hash table and the sake even and no you know from this so the condition cost a unit know to the sky or and all it's so you uh not you you shouldn't enough and uh the besides size is two solutions there is another interesting uh study which of flights of fragment only to with that you paid for and pair and the but in your two and P uh which is very efficient but the because the single operation cost oh with the fragment growing straight is five so the overall process time in this case almost a that in the previous of so in this study you propose applying a second stage had she to that duplicate that paris so that's the computer cost can be in your two and P we uh a lower single operation a you're of B we got each duplicate we can then to pair at the basic unit and uh we propose two hash functions uh to translate the temporal information into in prince so the first fingerprints is the temporal position a more right and in this case the just it is that to many it's so that uh uh uh as get of a fragments can be a a mapped to the same it is the neighbouring ring finger and uh the second you print is the temporal interval between the two of fragment and uh the is that second and based on these to different all pairs of uh a duplicate for not pairs i insert it into a two dimensional hash table and the uh but doing so that duplicate frame paris with high temporal is this since C can be ultimate sent into the same B so that the time-consuming cameras making can be avoid and the to detect a the high temporal sit we a a used would uh recurrence hashing histogram from the hash table and uh because that you hate a a friend of pairs with fight "'em" consistency have been but same boat into the same B so this in normally form a local maxima uh in this it and in this case the been embedding uh indicates that temporal duration of duplicate uh second so you eight sick is can be easily detected by uh searching for local mixing from uh hashing kids so this fall for the explanation of the proposed are reason and uh it's very simple but it's because we didn't you ever making on this yeah each you face and here and is that um hash table so that can see in if the be note and B which is much a lower than that of related stuff and we uh a better at the actress oh the proposed a reason by using a ten hour as and also uh one man stream and uh five years being were used for evaluating the vision and uh we i was you in both C and frame level the sequence level really phase how this side our yeah uh detect and the identify the commercial segments and of the frame that in right uh for precise at that are can local a commercial sick for example four from each frame the sequence start and the uh to be trained the sequence in of the results uh some right this table uh we implemented to state of the art that studies for comparison the first one for a right i five ring of light uh pairwise matching matching to with a duplicate kate for the pair and the second one proposed by green uh uh of lights uh pairwise matching to uh all sets of right or we can say all the nonzero the overall hash it's uh okay and uh able T are straight here in case our camp roll recurrence hashing of and a B here in the case of video to mine and uh for the statistics uh a P R F a respectively precision recall or and F one score uh the sub fix S and uh uh the subjects as means the frame the sequence table and F for the frame level find the T here in place of for that simple so from this table we can see that to uh our or uh uh of formant the baseline all right and and especially the much sort of the time uh them straight i uh oh the besides also implemented a an existing all you hashing technique for for reason and again in this case uh the propose are is out of from that the uh baseline uh uh for all current you have uh well yeah i've got introduce that's uh this baseline of light the frame uh a role in speech to that you kate a fragment pair and uh is not that uh up to this point to we in D V really of like a lot to the video and audio stream so one question here what we have to you be in the great these two streams uh so uh the we and audio streams can and the reading uh it frame level uh in the print the able or is and for efficiency and the scalability uh reason we propose in the region uh and integration add to that is a so from these table you of the of that's to the sequence level rick or the sequence that we also almost one hundred present in both places so this is my a to you in section variation to combine the detected a commercial second and that would or the uh rate of false alarms uh results in the number all misses and on that of the vision is that uh a uh in the case of frame level of innovation the preceding a is your T five or then the recall in both we do and all of is and this of this uh i by task a union vision to combine the detected commercials a frame and uh so uh the results a some fries in this table and we can see that's to uh the six as the devil it once for you to uh ninety eight point one percent and the frame level if one score in uh intro two ninety seven one four was uh so the them and without yeah demonstrated to the vector oh the proposed uh in separation street oh whether be applied uh the i reason to the one month we stream and the process and find was this and fifty uh i it's for we do and this then uh forty to me uh for audio so this again them stated that the height of regions oh our find the we applied to a of you applied our our isn't to a five here year string this stream was divide uh in into sixty one month sub streams and uh our our reason was in D V do already of to each sub string and we for formant uh how low computing and uh conducted a the recipes for used fifteen months for spread and the the final process time was this and twenty one oh you be that the person this you will be don't can see the commercial mining commercial detection we just some of the to take cost uh at least to five five years for us to so what i want to say our our yeah so we also conducted some statistics for each detected most so uh we hope this that he things could be have market research uh and uh commercial producers and uh uh complete company so this example is a beer promotion uh for this he's around the horizontal axis indicates the final day and the was co wine case the for a three so from this speaker we can see that a for this commercial there was no broadcast from two em to yeah yeah and actually this very somehow somehow go to stand so i of another one in this case the horizontal axis also in case that i'm of that but the what cool one indicates a they a week from a send a man they to set a so we can have so of a real data shape from actually when be for long as before a might a but actually know uh this very yeah data shape was also from from all other i'll call related commercials and we group of this find a be found the reason actually this is because in japan there is of what entry restriction reach probably bits the pro house uh of code or oracle our whole commercials from a five yeah two five P M on the and from five em to you have i am get so this is one of them and a example is of she's actually and uh we can see that this my thought was most a to be row house a a wrong five T M actually a so this i believe D V believe this is because uh five P M is of fine or the white i i with two what in or and the commercial have them uh no don't need to just by or Z or something and the the third example is a how a commercial and uh we can also the time zones is high a broad cross frequency uh for instance uh problem six the M two from six am to eight A and than most fathers having reference and the a scalable clock we that stop and uh after six P M uh when most fathers have finished their work and watching T V um so uh actually these observations can also be observed in this figure and the one explanation of this could be the car is okay it towards uh uh a rather than you know five and the not the explanation for be i know you most uh uh a japanese and the father Y i mean you in the money so the card either a a pretty for most uh popular T V vol for mail than female what uh so so that time limitation i'm going to us keep the can i like the this will you for a kind of change we a a i have a as we can jump then use a and not advertising companies going to use your technique need to to do some the marketing research uh currently be no yeah but to be a a considering to contact low companies and C uh better the are interested in our research and a normally i i i i don't think from things are interested T in this but uh uh actually there are more some commercial producers uh who uh that would interest in this work and the contract cost we are considering each a about the corporation of the collaboration uh true maybe we can provide some uh so is uh for so for so and i is or something like that or most a search yeah thank you the set point is stuff i i'm standing are doing the commercial we you pattern and discovery right so uh i think to extend you want to discover some out types also deals something like a um suppose we you all some do use we do yeah i and the should be do is a very special types of would be to with a specialised up at patterns uh you mean for detecting is duplicate be deal yeah all other oh i we maybe um of L out to depends on the type of that you could be was that you want to detect for instance uh in case of sports D also uh suppose we deal in "'cause" sparse we do they are some rib or cost of the sports aims for video uh of course a lot of channels that means in this case the the the but also almost same but in the news broadcast the the the the T V then use program produce a make at some uh a oh out or text captions in to the B also that the fingerprinting based straight she is not suitable for in this case and more robust uh technique is needed so this why i i i pointed out that that's this work is quite difficult is quite different from uh do near kate a detection it's a kind of exact duplicate detection okay i to so much thank you