Internet Video Search
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1 Internet Video Search Arnold W.M. Smeulders & Cees Snoek CWI & UvA
2 Overview Image and Video Search Lecture 1 Lecture 2 Lecture 3 visual search, the problem color-spatial-textural-temporal features measures and invariances descriptors words and similarity where and what data and metadata performance speed
3 1 Visual search, the problem
4 A brief history of television From broadcasting to narrowcasting ~1955 ~1985 ~2005 to thin casting
5 Any other purpose than tv? Surveillance Forensics Social media Safety Agriculture News Business ebusiness Science Family to alert events to find evidence / to protect misuse to sort responses to prevent terrorism to sort fruit to reuse archived footage to have efficient access to mine consumer data to understand visual cognition I have it somewhere on this disk
6 How big? The answer from the web The web is video
7 How big? The answer from as of May 2011
8 How big? Answer from the archive Yearly influx hours of video 1 Pbyte per year Next 6 years hours of video hours of film photo s
9 Crowd-given search What others say is in the video. We focus on what digital content says is in the video.
10 Problem 1: The variation So many images of one thing: illumination background occlusion viewpoint, This is the sensory gap.
11 Problem 2: What defines things? Tree US flag Language Aircraft Dog Suit Building Basketball Table Multimedia Archives Tennis Mountain Machine Fire
12 Problem 3: The many things This is the model gap
13 Problem 4: The story of a video This is the narrative gap
14 Problem 5: No shared intuition Query-by-keyword Find shots of people shaking hands Query Prediction Query-by-concept Query-by-examples What sources This is the query-context gap
15 This image cannot currently be displayed. System 1: histogram matching Histogram as a summary of color characteristics. Swain and Ballard, IJCV 1991
16 1 Conclusion As content grows, many applications of image search. Deep cognitive and computer science problems. With simple means one gets visually simple results.
17 2 Features
18 Source. reflection Light source e() Object ρ() Result e( ) ρ( )
19 (R,G,B) ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( = ρ ρ ρ d f e d f e d f e B G R B G R
20 = B G R B B G R G B G R R b g r (r, g, b) in (R,G,B) Independent of shadow!
21 The sensation of spectra Hue: dominant wavelength (E H ) Saturation: purity of the colour (E H - E W )/E H Intensity: brightness of the colour E W E H E W white green
22 Human perception combines (R,G,B) response of the eye in opponent colors Maximizes perceived contrast! The sensation of spectra: opponent + + = ) (2 4 1 ) ( 2 1 PuperGreen BlueYellow Luminance G R B G R B G R
23 Color Gaussian space E E E 0.06 = R 0.35G 0.17 B Maximizes information content! Geusebroek PAMI 2002
24 Color Gaussian space (R,G,B)-pdf (E 0,E,E )-pdf
25 Matter body reflectance in (R,G,B)
26 Taxonomy of diff-image structure T-junction Junction Highlight Corner These junctions later bring recognition
27 Gabor texture The 2D Gabor function is: h( x, y) = 2 1 πσ 2 e 2 x + y 2 2δ 2 e 2πj( ux+ vy) Tuning parameters: u, v, σ Manjunath and Ma on Gabor for texture in Fourier-space
28 Hoang ECCV 2002 Gabor texture K-means cluster of RGB K-means cluster Gabor opponent
29 Gabor GIST descriptor Calculate Gabor responses locally Create histograms as before Distinguishes things like naturalness, openness, roughness, expansion, and ruggedness Slide credit: James Hays and Alexei Efros Olivia IJCV 2001
30 Receptive field in f(x,t) Gaussian equivalent over x and t: zero order first order t Burghouts TIP 2006
31 Gaussians measure differentials Taylor expansion at x For discretely sampled signal use the Gaussians The preferred brand of filters: separable by dimension rotation symmetric no new maxima fast implementations.
32 Receptive fields: overview All observables up to first order color, second order spatial scales, eight frequency bands & first order in t.
33 Carson PAMI 2002 System 2: Blobworld, textured world Group blobs based on color and Tamura texture User specifies query blob and features System returns images with similar regions
34 2 Conclusion Powerful features capture uniqueness. A large set is needed for open-ended search. The Gauss family is the preferred brand of filters. Fast recursive implementation: Geusebroek, Van de Weijer & Smeulders 2002
35 3 Measures and invariances
36 The need for invariance There are a million appearances to one object The same part of the same shoe does not have the same appearance in the image. This is the sensory gap. Remove unwanted variance as early as you can.
37 Invariance: definition A feature g is invariant under condition (transform) caused by accidental conditions at the time of recording, iff g observed on equal objects and is constant:
38 Quiz: scale invariant detection What properties are invariant to observation scale?
39 Color invariance C c b () e() n s v ) f C ( = m n s e c f d + m n s v e c f d b(, ) ( ) b( ) C ( ) s (,, ) ( ) s ( ) C ( ) surface albedo illumination object surface normal illumination direction viewer s direction sensor sensitivity scene & viewpoint invariant scene dependent object shape variant scene dependent viewpoint variant scene dependent
40 Matter body reflectance in E
41 C is viewpoint invariant c R, G, B) = arctan 1 ( R max{ G, B} c R, G, B) = 2 ( arctan G max{ R, B} c3( R, G, B E space C space Gevers TIP 2000
42 Hue is viewpoint invariant H = arctan 3 G B R G + R B, H is a scalar
43 Differential invariants C, W, M C is for matte objects and uneven white light: E E C = 2 E E E E E C E E C x x x = = W is for matte planar objects and even white light: E E W x x = E E W x x = M is for matte objects and monochromatic light: Geusebroek PAMI E E E E E N x x x =
44 Retained discrimination shadows shading highlights ill. intensity ill. color E H W & W C & C M & M L E 990 H 315 Retained from 1000 colors σ = 3: W 995 C 850 Geusebroek PAMI 2003 M 900
45 3 Conclusion Know your variances and invariants. Good invariant features make algorithms simple.
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