Přepis řeči - SEARCHING IN ONE BILLION VECTORS: RE-RANK WITH SOURCE CODING

0:00:13	you
0:00:13	introduction election
0:00:14	so is
0:00:15	don't work was the formant that now i'm we use as you have then
0:00:19	but that is
0:00:20	from yeah a on the
0:00:22	on the risk of ones i'm said i
0:00:26	so this took okay a an interesting in the large
0:00:29	the base
0:00:30	of media
0:00:31	uh the nuns
0:00:32	uh new image sell to large that the base
0:00:36	day
0:00:36	give a means that when you
0:00:39	on in state of the your the
0:00:41	scheme
0:00:42	an image
0:00:43	a your you present the by
0:00:45	about one thousand or two thousand
0:00:47	so it means that we have to on the
0:00:50	to be billion
0:00:51	uh descriptors
0:00:52	on the the the basis get those
0:00:54	oh sift descriptors we
0:00:55	or
0:00:56	uh of them mentioned one like
0:00:59	uh if we look at a do so much
0:01:02	uh we would like to
0:01:03	right
0:01:04	and
0:01:05	a thousand i well so
0:01:06	video
0:01:07	for instance and trying and evaluation task
0:01:10	as a
0:01:11	that a two hundred hour
0:01:13	on this to do or also present it by billions of what you want your
0:01:19	and and some in music retrieval or uh was really is the uh
0:01:23	the column down you some that
0:01:24	the base
0:01:25	uh on the gains all the it is one billion
0:01:31	oh you can take a more concrete example of uh are
0:01:35	spatial to that's like V
0:01:37	uh evaluation in the copy detection test
0:01:40	uh uh we have extra from do about sweet that's five billion image is
0:01:45	thus
0:01:45	two represents a a a a a of the database
0:01:49	on on a D
0:01:50	uh about uh
0:01:51	one don't quite fourteen million uh we do it
0:01:55	and look do is
0:01:57	we want to sell some query
0:01:59	uh
0:02:01	zero on look whereas as or of the base
0:02:03	based keep those
0:02:05	which mean
0:02:06	is that
0:02:06	for each disk
0:02:07	though
0:02:08	we look at
0:02:09	nearest neighbor or
0:02:10	we Q
0:02:11	D at that so it could and distance
0:02:13	a for the so
0:02:15	now if we look at uh exhaustive even now search
0:02:18	discarding and talk
0:02:20	uh
0:02:21	we have a one for
0:02:22	frame described i
0:02:24	but one thousand descriptors
0:02:26	we we have to make trillions of
0:02:28	i mention that i
0:02:29	vector vector reason
0:02:31	that is in the order of ten our four
0:02:33	the remote through
0:02:35	so we can do it
0:02:36	for single
0:02:37	frame
0:02:38	why we need for our powerful
0:02:40	approximate to make the search research
0:02:42	which are situation
0:02:44	she's quite to used to avoid program
0:02:46	but also so memory efficient
0:02:48	okay i i wouldn't miss is or
0:02:52	so uh uh such a reason as to to nice
0:02:56	i to to re uh uh from speech yeah
0:02:58	a a a pretty C
0:03:00	it's great quite to use that to retrieve though should be actual
0:03:04	you as neighbours
0:03:06	just be the
0:03:07	is why we make approximate search
0:03:10	but also the a we use that
0:03:12	and actually most or and many optimize to first protect
0:03:16	for instance look at it is
0:03:18	steve division
0:03:19	uh which is very popular "'cause" that's some good
0:03:21	so sort you corpora properties
0:03:23	but it is quite a memory
0:03:24	and swimming
0:03:25	as it is is that you use only at ten at table
0:03:29	uh you need at least four to by per vector
0:03:32	or the exact
0:03:34	to minimum
0:03:35	and using that you have that some very good
0:03:36	performance because i is no that that that at the station
0:03:40	in the let's see
0:03:41	uh
0:03:42	a but does choice be two
0:03:44	select a finite where is an which is a very good was and the terms of the actors
0:03:49	right
0:03:50	a state of so
0:03:51	yeah is yeah agrees um
0:03:53	on this is now a great
0:03:54	so you you you agree
0:03:56	but again the agrees reason
0:03:58	cry as a lot of memory
0:04:00	so yeah made in experiment
0:04:02	on on need when we and vectors
0:04:04	on choose about one hundred
0:04:06	in me given rights
0:04:07	index
0:04:08	only when you don't like
0:04:11	so it can approximate socially so that done uh
0:04:14	you know i on two stage
0:04:17	a the first stage is approximate search it set
0:04:21	well you have some kind of space but training
0:04:24	so this is a a H like a space of the shouldn't base and i can
0:04:29	on you can
0:04:30	for a given vector
0:04:32	the find a set
0:04:33	uh where is a vector or lie
0:04:36	so you have to do this for so that the basic though
0:04:39	a uh a flying
0:04:40	and when you have a crappy
0:04:42	you compute the
0:04:43	that's key which you say
0:04:45	on the you you would five
0:04:48	the vectors of the same set as put control
0:04:51	nearest neighbor
0:04:52	so this is one as a function on in the less age
0:04:55	you have sit the hard partition this
0:04:57	to improve the probability
0:04:59	to get a
0:04:59	you
0:05:00	nearest neighbor
0:05:02	and then
0:05:03	when you have some potential nearest neighbor but men mean as and are not a very good
0:05:08	as a very far from the query vector or actually
0:05:11	so that is why you speaker lee
0:05:13	uh made
0:05:14	a verification
0:05:15	based an exact
0:05:17	it two D
0:05:17	this calculation
0:05:19	know the are two uh a a a a is
0:05:22	approximate net middle
0:05:24	a good thing is that is it should nearest neighbor
0:05:27	oh uh would the
0:05:29	from the first stage
0:05:30	you sure are that that will be wrong in good position of does exact
0:05:35	a speculation
0:05:37	the the problem is that for this a we don't stage
0:05:40	we need to the script dolls
0:05:42	on
0:05:43	it means
0:05:44	that we i still as to use a a huge amount of memory
0:05:49	so for instance for one B vectors
0:05:51	it means more than one hundred to get that we
0:05:54	i so you have to but and this
0:05:56	and this
0:05:56	in this case the in back
0:05:58	the efficiency of this scheme because
0:06:00	in practice
0:06:01	you have to check too many
0:06:03	nee both there
0:06:07	oh you cannot do it and the but it up
0:06:09	to to not more than one
0:06:11	vectors
0:06:12	was
0:06:13	what we propose a paid not is to add that the at and that if we want came is that
0:06:17	based on the source coding
0:06:22	be for i have to introduce a a a a a a previous work
0:06:25	you may
0:06:26	uh on a makes cell edge
0:06:28	where we use
0:06:29	um
0:06:30	compression base
0:06:32	approach
0:06:33	that is we are going to represent
0:06:35	each that base vector
0:06:37	by a compressed
0:06:38	presentation
0:06:39	that is
0:06:40	case a concise presentation
0:06:43	on
0:06:44	uh is
0:06:45	done using a product a quantizer to have many
0:06:47	prediction values
0:06:49	available don't
0:06:50	he a bus
0:06:51	for
0:06:52	a production value
0:06:54	i is really
0:06:56	and then to search is is seen as a distance sounds
0:06:58	approximation problem
0:07:00	at is
0:07:01	uh instead of
0:07:03	computing to just distance between X and Y
0:07:06	you are going to approximate it
0:07:08	base this sounds using I
0:07:10	and of
0:07:11	of why
0:07:12	on that you have a bias can use to make so but E
0:07:15	we about the by is there
0:07:17	just the most
0:07:17	but
0:07:19	and
0:07:20	on so in
0:07:21	pressing put that C
0:07:23	that we can make this distance
0:07:24	estimation directly in the compressed domain
0:07:27	do not at and compress that that to make
0:07:30	it to so
0:07:31	uh that
0:07:31	power
0:07:33	on uh you may be from yeah with
0:07:36	uh you we
0:07:37	uh on building this that where a a a a couldn't vectors
0:07:40	on that
0:07:41	to you a space
0:07:43	to P place to it to distance by hand this sounds
0:07:46	on
0:07:47	using this scheme
0:07:48	you obtain
0:07:50	almost same efficiency
0:07:51	the performance
0:07:53	much better i can show you some
0:07:56	or so we have some proved average above bounds
0:07:58	sounds
0:07:59	estimation people
0:08:01	so now uh
0:08:03	i'm looking is a on stage
0:08:05	the re-ranking king stage knowing that the first stage was a compressed base
0:08:09	pro
0:08:11	a good proposed but use the first stage is that we had a compressed based they sink which means that
0:08:16	for each that that is vector or we have an explicit a construction
0:08:21	of Z stick to
0:08:23	so
0:08:24	instead of using the whole disk
0:08:25	to for the second stage
0:08:27	we are going to uh we find
0:08:30	the first a construction
0:08:32	or that the basic to well thank as the first
0:08:34	state
0:08:36	this is done by first computing still this your or a vector
0:08:39	so
0:08:41	it means
0:08:41	this this
0:08:42	a small vector
0:08:43	we two
0:08:44	oh this one
0:08:47	and then it's spectral because we do not want to stop me because he does a i didn't mention a
0:08:51	uh uh uh for C what be don't we
0:08:54	eight
0:08:55	it's does not quantized use a using the quantizer that at like to
0:08:59	uh uh uh if it's dual uh
0:09:03	so now means that
0:09:04	we can approximate Y as a first approximation obtained by the first stage
0:09:09	plus as a gone
0:09:11	uh
0:09:11	of if five don't
0:09:13	the could yeah
0:09:14	so that is on could be to this dual vector
0:09:16	which improves the initial
0:09:18	the estimate
0:09:19	both as or a construction but also for the distance calculation
0:09:24	and we can uh
0:09:26	re
0:09:26	we can
0:09:28	right that
0:09:28	uh between precision and memory
0:09:30	by
0:09:31	is the amount of by to all going to D to this
0:09:35	for contains so
0:09:37	so it's parameters and prior on can be eight bytes
0:09:40	that
0:09:41	which just a small and the number of bytes
0:09:43	to "'cause" on
0:09:44	so a which in not vector
0:09:47	that's good that i was them which is
0:09:48	quite quite simple
0:09:50	so
0:09:51	we yeah that that this vector
0:09:54	why
0:09:55	the first approximation made by the first stage
0:09:58	E
0:09:59	a consists in a pleasant thing it that the code that
0:10:03	which can be seen in the old net clean space as Q of why
0:10:08	is the first stage well bring to compress the curvy
0:10:10	we suppose of possible to Y
0:10:13	on sale a shot is
0:10:14	oh vectors
0:10:15	so once that we want to fine
0:10:18	if Y to select T as a put concerned roast neighbour or
0:10:21	is you are going to explicitly to construct
0:10:24	this
0:10:25	improve estimate
0:10:26	that is we been to uh we find
0:10:29	uh why by using so was really taught like this
0:10:33	so we have a hats
0:10:34	and then is a new distance to i will be
0:10:37	are there in instead of this C
0:10:39	which is a better approximation
0:10:41	uh is than
0:10:42	the distance between back uh
0:10:44	so this is a better a use this
0:10:46	for this
0:10:47	to this
0:10:51	so is it see some such results
0:10:53	in one billion vectors
0:10:56	the
0:10:57	in this case we is used for the first stage
0:10:59	eight a of vectors
0:11:01	the first one
0:11:02	that wasn't a of the the the cost
0:11:06	on just use this performance is performance
0:11:09	yeah i i shows the wrong of
0:11:11	so one else need or when i don't get the right
0:11:14	over a a large amount of query
0:11:17	oh
0:11:17	the probably for we no stable is one in the first position
0:11:21	in the text first position in the hundred as position
0:11:25	you have to sing that the wrong can be what up to one billion vectors
0:11:28	to or that you can see that the first approach
0:11:31	is a better
0:11:32	i could be to wrong
0:11:34	so the neighbor in the first thousand position but
0:11:37	that's not
0:11:39	so you make and we don't king using one the eight by
0:11:42	for of it you as usual uh could could cause a and want to
0:11:46	we set
0:11:46	you get a very good improvements
0:11:48	i then you can file
0:11:49	i sixteen bytes
0:11:51	but
0:11:52	and we converge
0:11:53	the using a uh one of twenty eight byte
0:11:56	not perform ones there
0:11:57	which means that
0:11:58	the first
0:11:59	a a stable always one and first position
0:12:05	okay on
0:12:06	uh i have say that so we don't state as a unit cost
0:12:09	which is almost negligible compared to the first
0:12:13	on as a clone second so
0:12:15	a means just mention
0:12:17	oh kind of ms so that we can increase in new stable
0:12:20	uh uh we see less and one minutes again
0:12:22	oh a a to two hundred means going if you want to be sure that you we gets a nearest
0:12:26	stable
0:12:27	one billion vector
0:12:28	a very five
0:12:30	uh so
0:12:31	you to of the old king is that we you can see
0:12:34	uh i'm not being that was a it
0:12:36	time
0:12:37	but is better to use a less i as the first stage
0:12:40	on more of the circle because
0:12:42	you will have a i efficiency
0:12:45	we have that separation for the first stage
0:12:47	uh but
0:12:48	comparable precision or or in fact
0:12:51	if an improved precision using the we don't king
0:12:54	a stage based on on uh
0:12:55	source couldn't or fine
0:12:58	and i would like to mention the before can might talk
0:13:01	that yes but then nine uh be vector that the set of one billion
0:13:06	big
0:13:07	on the reason we have don't this
0:13:09	is because
0:13:10	as many papers thus an approximate cell
0:13:12	is that makes an evaluation on one billion vectors
0:13:15	and one in fact uh i think i have shows is the beginning is that's actual sites get a petition
0:13:20	we need to on that one big in fact on that one you
0:13:23	so if you makes make three months
0:13:25	these thirty days and on the here uh
0:13:27	where are but the set
0:13:28	and this is one for which
0:13:30	uh we have uh
0:13:32	we compute that
0:13:34	so uh extracted and thousand prairie
0:13:37	we this and because for long
0:13:39	on you they completely the exact
0:13:41	nearest neighbor
0:13:42	that is for each we
0:13:43	you have computed support supports distance is
0:13:45	a vectors
0:13:47	on we give
0:13:48	zero on and i wrong of uh as a
0:13:51	a true
0:13:52	uh a a thousand hz an on the corresponding distance
0:13:55	C
0:13:55	case you want
0:13:56	we
0:13:57	but make runs
0:13:58	how
0:14:00	to conclude my two
0:14:01	we have proposed the
0:14:03	as could base you don't king approach
0:14:05	that the vote using a whole skipped also you can see
0:14:08	it
0:14:08	a to a the memory for a comedy yeah the server
0:14:12	in which improves with shades
0:14:14	yeah
0:14:14	a trade off between a efficiency and pretty
0:14:17	for a fixed more is that
0:14:19	you have a is uh the to at a point of the vector for evaluation
0:14:23	of approximate search
0:14:25	a not so i have to munch that we have but the method a cage uh a a nine
0:14:30	a for compression based me sets that
0:14:32	produce
0:14:33	as a result of uh
0:14:35	the and i where is an and so
0:14:37	source but things that i have a mention
0:14:40	a for you
0:14:47	a a question
0:14:57	i
0:14:57	i have very short uh question how deep and is this a technique on using set per se
0:15:04	so
0:15:05	a a do we have to use it with this technique are uh no so we we have this this
0:15:10	is this so don't the us to a the loss and we do this skip those on the back of
0:15:15	all descriptors
0:15:17	but and you and i think that they can be you can
0:15:20	done this
0:15:25	and i have a question
0:15:29	i i i i i have one more question myself actually
0:15:32	and
0:15:33	you do this iteration with one
0:15:36	and what prevents you from a to reading again and we can it for them for further and you
0:15:42	given T conversations that were that one if you make the quantisation of zero very small yeah i can go
0:15:47	to zero uh for so if were of course on it is a good question because of uh we
0:15:51	out to optimize
0:15:52	the first stage the second save on i'd use that stage can think of right
0:15:56	stores and that some kind of words are sufficient to asian actually so we have that the there's two up
0:16:02	and to this stage
0:16:03	so that is
0:16:04	would be a good a
0:16:06	it's nice
0:16:07	or several let us on try out many neighbours how many back to give to and yeah yeah
0:16:13	but you you you get you have that
0:16:16	i remember my first course and quantization and and this per some she make is that the quantization is you
0:16:21	know for
0:16:22	with a bin
0:16:23	sorry i i remember my first course score quantization and you know the assumption you make
0:16:27	it that you know that that the air the quantisation here use uniform within a bin
0:16:32	um
0:16:34	so that there's
0:16:35	you know you know form here
0:16:37	and so they can be evenly distributed i you know points in the bin
0:16:40	and so i'm wondering is if you do one or more levels of quantization
0:16:45	wouldn't that make
0:16:46	um a very hard to quantisation because there's is a very little structure left to you know it's
0:16:50	take advantage of
0:16:51	yes
0:16:52	uh
0:16:54	i think that's true but and you at some point when you use and are very fine can ties also
0:16:58	just as the the first
0:17:00	the yeah
0:17:01	side is correlated for the eigenvalue value
0:17:04	like compression
0:17:05	yes some structure
0:17:06	for
0:17:07	a big intensity
0:17:08	when you we find as the and we when you a noise anyway
0:17:11	we have the same
0:17:12	uh
0:17:13	a party
0:17:14	i think that uh as in compression that is
0:17:16	as the on which you are we code
0:17:18	uh
0:17:19	a most one them uh
0:17:21	so you from side or
0:17:22	but the program from yeah it's
0:17:25	just just got but it is a problem when you have a high dimensional data set
0:17:28	because all points weekly descent
0:17:30	one one uniform made you yeah you you mean you a this so a to compared to
0:17:35	and you could do
0:17:36	but the
0:17:37	if by this
0:17:39	improve
0:17:43	i think
0:17:45	i

SEARCHING IN ONE BILLION VECTORS: RE-RANK WITH SOURCE CODING

Image and Video Indexing and Retrieval

Přednášející: Hervé Jégou, Autoři: Hervé Jégou, INRIA, France; Romain Tavenard, University of Rennes 1, France; Matthijs Douze, INRIA, France; Laurent Amsaleg, CNRS, France