Chain Codes, Area based Retrieva, Moment Invariants, Query by Visual example (05.05.2011)

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Chain Codes, Area based Retrieva, Moment Invariants, Query by Visual example (05.05.2011)
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10.5446/336 (DOI)
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Technische Universität Braunschweig
Institut für Informationssysteme
Balke, Wolf-Tilo
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In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
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Point (geometry) Surface View (database) Computer-generated imagery Curve Online help Shape (magazine) Computer font Area Neuroinformatik Web 2.0 Term (mathematics) Different (Kate Ryan album) Object (grammar) Hybrid computer Representation (politics) Histogram Matching (graph theory) Characteristic polynomial Computer simulation 3 (number) Cartesian coordinate system Shape (magazine) Similarity (geometry) Digital photography Computer animation Logic Personal digital assistant Knowledge representation and reasoning Right angle Object (grammar) Representation (politics)
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Point (geometry) Ferry Corsten Scaling (geometry) Decision theory Multiplication sign Invariantentheorie Letterpress printing Matching (graph theory) Shape (magazine) Disk read-and-write head Mereology Rule of inference Rotation Neuroinformatik Measurement Active contour model Endliche Modelltheorie Computer-assisted translation Domain name Translation (relic) Measurement Shape (magazine) Similarity (geometry) Arithmetic mean Computer animation Different (Kate Ryan album) Website Form (programming) Reading (process)
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Point (geometry) Pixel Building Codierung <Programmierung> Tournament (medieval) Workstation <Musikinstrument> Similarity (geometry) Electronic mailing list Shape (magazine) Machine code Computer icon Number Permutation Chain Roundness (object) Knowledge representation and reasoning Different (Kate Ryan album) Inverter (logic gate) Symmetric matrix Quicksort Metropolitan area network Information Electronic mailing list Shape (magazine) Permutation Number Computer animation Chain Right angle Quicksort Convex set
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Point (geometry) Digital electronics State of matter Ferry Corsten Real number Direction (geometry) Maxima and minima Shape (magazine) Mereology Tangent Area Cartesian coordinate system Skeleton (computer programming) Different (Kate Ryan album) Touch typing Set (mathematics) Boundary value problem Circle Symmetric matrix Area Point (geometry) Maxima and minima Bit Line (geometry) Rectangle Shape (magazine) Skeleton (computer programming) Number Computer animation Circle Personal digital assistant Website Video game Musical ensemble Quicksort Boundary value problem Row (database)
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Probability distribution Functional (mathematics) Distribution (mathematics) Moment (mathematics) Modal logic View (database) Multiplication sign Mass Infinity Sign (mathematics) Goodness of fit Set (mathematics) Random variable Personal identification number Distribution (mathematics) Real number Discrete group Moment (mathematics) Gradient Skewness Number Stochastic Computer animation Factory (trading post) Right angle Musical ensemble Kurtosis Resultant
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Pixel Functional (mathematics) Distribution (mathematics) Ferry Corsten State of matter Moment (mathematics) Direction (geometry) Mass Pointer (computer programming) Different (Kate Ryan album) Single-precision floating-point format Uniqueness quantification Descriptive statistics Adventure game Area Distribution (mathematics) Inheritance (object-oriented programming) Moment (mathematics) Euler angles Process (computing) Computer animation Function (mathematics) Theorem Video game Spacetime
Probability distribution Point (geometry) Functional (mathematics) Pixel Distribution (mathematics) Transformation (genetics) Moment (mathematics) View (database) Computer-generated imagery Invariantentheorie Translation (relic) Insertion loss Shape (magazine) Mass Rotation Neuroinformatik Mathematics Different (Kate Ryan album) Uniqueness quantification Pixel Zentrales Moment Descriptive statistics Area Rotation Distribution (mathematics) Matching (graph theory) Clique-width Discrete group Moment (mathematics) Machine code Predicate (grammar) Shape (magazine) Digital photography Computer animation Theorem Summierbarkeit Coefficient Reading (process)
Point (geometry) Transformation (genetics) Moment (mathematics) Scaling (geometry) Multiplication sign Invariantentheorie Control flow Design by contract Translation (relic) Mass Event horizon Neuroinformatik Number Database normalization Protein folding Centralizer and normalizer Different (Kate Ryan album) Queue (abstract data type) Software testing Endliche Modelltheorie Data compression Zentrales Moment Area Rotation Context awareness Information management Moment (mathematics) Variable (mathematics) Computer animation Personal digital assistant Factory (trading post) Linear map Spacetime
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Probability distribution Functional (mathematics) Moment (mathematics) Invariantentheorie Information retrieval Goodness of fit Invariant (mathematics) Average Different (Kate Ryan album) Alphabet (computer science) Vector space Vector graphics Energy level Multimedia Physical system Distribution (mathematics) Invariantentheorie Moment (mathematics) Database Perturbation theory Cartesian coordinate system Computer animation Alphabet (computer science) Information retrieval Universe (mathematics) Information systems Game theory Identical particles Physical system Local ring
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Uniform resource locator Computer animation Computer-generated imagery Query language Visual system Price index Database Process (computing) Matching (graph theory) Binary file Representation (politics) Abstraction
Standard deviation Image resolution Direction (geometry) Gradient Computer-generated imagery Point (geometry) Calculation Price index Computer animation Query language Pixel Abstraction Local ring
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Direction (geometry) Computer-generated imagery Visual system Calculation Database Correlation and dependence Matching (graph theory) Counting Similarity (geometry) Information retrieval Computer animation Cross-correlation Query language IRIS-T Block (periodic table) Pixel Summierbarkeit
Codierung <Programmierung> Moment (mathematics) Computer-generated imagery Visual system Database Shape (magazine) Emulation Area Information retrieval Chain Invariant (mathematics) Computer animation Query language Moving average
Information retrieval Computer animation Lecture/Conference Information
Last lecture we were trying to figure out how to work with she in what makes a shape up to 6 men shades of of images were talking about not basic interaction with images way just of defined vessels and say you know like are not of the images made be on to some kind of like a part of the images may be background or the ex object of wrestling algorithms I'm on the other hand were a kind of discussing out what makes an engine edge so was part of become too and what is not and were talking about the gradient of based reasons to to to find out where they are shift significant shift in the intensity distribution and set to shift makes for a good edged because it has shifted the intensity distribution I'm also are all vigil at perches and can can be recognised the shifting can see what happens in the image and that means what the value of Oakland to a few conceded the I'm building on segmentation in that way was kind of good segmentation and we can see some of the pictures which can meant The big images quite well you know what we see the basic traits of the of the image and the Duke will and talking about metallurgical operate as a little bit so cleaning the data removing some Moyes I'm a filling Holmstrom in income but where we did not talk about and lost pointed out at the end of the lecture is that we still have no way to represent shape shapes but with no idea how to stop them what we do with them and this is what to do today big we go into shape based features so what is the actual feature the which full representing which which are about some very simple but features like chain codes C a chapter C a and the and the want to Dubai example just to see what what happens somehow be straight features and can be used
This is C a Areas some help about figures out so if you got active Qantas Allwood lost a talking but Watership turned from Asian get this image that is kind of like shows Ali's lit bullet lines
That it would you basically have an image Which does not help you to much of the time this and all we have to now we have to see about what to do with the shape but we of extracted from the and as well as we could either represent the individual objects so we found a phase in the pictures that represent the space as this is the fate of her that we could benefit represent the entertainment and what your own images from the day with whether to faces on the winner of this does not only needs the reputation of each individual shape but also the connexion of the shape of suspected each of was something like a tree which could be kind of like something here in the UK like for like a boy he obtained so the different shapes 1 could be drawn down he said Rodrigo shape way and the other 1 but this Welch's thing appeal which is good but not good but there was together make the tree in the likes of the Hold image Some information carry some Syms's some more semantics and if we go down to describing individual objects of all the images with the of something that is at stake in the than the and we basically talk about the description of a column to those on 1 soloist as and shaped and this is closed growth usually that we could save it will be area that look like a tonight not become to may be occluded somehow but the timing and to have a big area and the and the and the and the and the something that something like like the trunk is very specific the find the area has a certain from the area and that is a big difference brought over the and there the of a simplistic and the but you could also take a basically the shape announced that there must be something like this trying to steal area here to make the elephant and as a pin sozzled the description of the shape off the area factories carry some some information And some of us would you might after not end up with is you use a lot the state of intellect but that takes some information from the area for example but the area is full lodges can immediately distinguish hummingbird from from an elephant but the respective size of the area coming but will take a little area where the elephant with the help of the space And to get a was that you could also take something of a curse some characteristics of this kind of hybrid representation taking area based and come to a based features into account of what I do with the Intel image tonight would you do if you don't want the individual Object only the elephant but you just say you might from the image has a certain pattern of edges and this is why want described the this for a long to retrieve The body basically do there is that you figure out what the dominant and 1 of the early recognising part of the of the picture the out all the noise and then work on the edges such and we can do the same thing basically wooded with the colours and some of cost comparing the image pixel by pixels in terms of colour was very tedious talks with in want to do that sort but we do we use his to grab a we use of OK so much yellow and so much to them Madrid the 1 of the in the White and we do the same with with the edges basically so much edges of certain Langford somewhat edges of certain direct in the image with don't care where they actually are But it won't read faces you not like that would have 3 oval-shaped somewhere in the image and the skies we all images of 3 faces and also in the tune of 3 eggs so and so We are losing information in leg by the by going to the aggressively definitely information that it gives us a on and more efficient ways of Comparing images on far better than point-by-point pixel which doesn't help you the images shift just won't accept left of something I Molecular takes about pixel of compilers of the same name will result in basically Australia said that can do this kind of Her book So this kind of how we represent shapes all plentiful today when we want to talk about but firstly says the document which is very good for the recording By the way idea K was a recording said that helpful
UK Good so end If we have the shapes In some images with Sigmund the match correctly and weekend interpret the shake was Different to the notions of all familiarity images we could help ways look for images was simply shape up the basically the images that contained and somewhere the date the Gray and went in the middle the picture and all the photo off some hit with an elephant toy like a touch and go as a No something that as long as elephant shape It's OK and in the pictures that does not mean however that the 2 images of the same perception loathsome levity The image of a man and In certain It is totally different from perception a point of view And the image of kids following a blue clash at a pig and you get you get on with the difference between the 2 of them are similar in terms of an elephant of some kind of being in the pink The could save known all value on images was simply dominant shape enough at don't care about If there somewhere is an elephant in the case told the and a 3rd of their value as and on some advertising campaign posters in the background of the movie was a Web but rather on images that are similar in a perceptual said that it was a big and in the middle of the picture it out and get it out again again with books 1 hour but were enough that it looks very similar to me up in the Derby and shapes Are the same And this is actually a totally In put which What we do in representation and will be doing simulator 2 matches and it is just different notions of American and but fact the value you can ideas about this now no right all right The best possible and depends on your reputation for you want to have what you want to see And that was a reasonable ideas and king and a meaningful definition of Logic you want SAS or which depends on the particular application found a way if we go to country based comparisons from than we find all the images was similar shaped objects and the outline refugee just seen as close computer and you grow like that would be good for you need to take them
Fadse a not not to get and elephant shaped close come to of just take and whom away drawing the wrong thing about so that you know on the might of the funds would be made nice so with the money they were but the And you can get those close to of basically of for segmentation out what we've seen in segmentations that for example you remove the from last time that the the contains very many different Pattinson because of the shady of of the skin and then and part like legs so it was all so different colours on the back of streets of sometime Street and and and I thought this is most took the lead segmenta to death United was not live shaped it was this which talks but and pieces of that even on the and and ideas and and and and some straight from the back the and this has of this was a more left it to the very often aloft not by determining the actual shape that he won the FA difficult thing from 6 am But the semantics at all success with the bat described such a common to than just by looking at the the edges and the image and where walked so that kind of like chokecherry semantics of the same and if we look at it and matching of shapes celebrity measures that we have just had a new comedian distance or something pixel wise comparison is not work any more because when comparing shapes would you agree that the state of the same all the difference
But seem to be saying the and the pixel was comparison does not period because this 1 is bigger And they'll want this 1 is kind of Shifted removed just about 0 rotated by some degree but still sustained shade sustained calm and what we need is a simpler to measures that is in the air he was respected should of metal where the Apple or Corus on non on the picture which was paid to scaling the mesh big the Apple actually with a small at political with what you can do upload movement And it should be very and was the 3rd rotation And that's not easy to build a huge something that we after best summed some kind of intelligence into to make your measure his American measure invariance to those 3 Linear from the book a this
But we want on the questions it and what as with some of the public part of that But it's it and if you ask me I'm if you can You don't something like he occluded and then you say or what is the apple shape the Bush it obviously like that and then it goes in the UK They have to possibilities you can say well and this is not ambush a with this kind of like a take a bite out of the Apple something like this different and of course it is the semantic to decide whether an Apple is just cluded that whether there really is a piece missing and whether it's supposed to be that way again if it but it's not a decision that computer can make And you can you can all was Colin member experience and say no apples around and the something that Apple shaped but by missing a all with completed but then you're making rules of to interpret the image The images show A print But is there something missing in their something uprooted them well at this point we have to live with it A print Yes Part of the microphone ferry But each year the teaching that If you like for Apple like exactly That's not the way without having a read I model of the elephant that I can build a I'm and a measure of the to measure that really matches the site few of knowledge and the head of the will of and the review of its just possible without having to complete model of the of the and So purely from images you come to that because and as you say the perception that it looks totally different From and seeing a cat from the back of the book from the bad May leave you and out what it actually is Seeing them from the front Will probably not be fell a means something that looks at City left from from from certain a deal on This definitely not looks to move from and that shows that the building a similar to measure that can reduce the which between these things that is not an easy time without having domain knowledge
So all we can do is kind of like we can we can talk about Linear transformations very simple Linear information making things bigger shifting things space rotating things and where we can say 1 working with kind of based images is that the wisdom that the image gives his W different we can see that he's damning with the circus shape and this question and we have the squad shape here and the way in the shape yet again with the same shapes the visual impression of the images of a different kind
It says 1st talk about some of the features that we could use to focus on all that we could use to represent some of the kind to feature and 1 of the things that we could talk about the number of overseas so it is it They shape that goes in 1 smooth lines policy points I'm Another possibility is what area actually closed By the shape of the shaped like that it is close law of area if became shaped like that Enclosed very little area of the length of the to of what is probably the same or even longer than the case of So basically high all over what would amount of areas in close and you can almost argue about the holes and some shapely say OK this distinctly was this year and closed a lot of area what if something like the same size like this with this being the shape of the world Distinguish under different Gallen all those round of 3 of the 4 Grand has along Area included the other 1 has not so you can or you want the also talk about eccentricity and stuff like that of how stop like the shape 0 elongated is the shape of the typical indicators of for so a week and say this is more squalor this Thesis more rounded in this is eccentric this century And of course indicators of could be used for a 1st indications depending Mauriac if I'm really my application is to distinguish not from by as the last of its easy to use some of these represent the shapes because and offers roundish and the ball would is long dated so measures simple measure very simple measures like the eccentricity of the shape But why would I be sufficient distinguish between the 2 Of over can not distinguish elephant from the night of from the board and of the Met competition 8 gets and the more different shapes in semantic me to her The more complex the measure Sometimes load of the features to could bombs
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