What is Texture? Texture depicts spatially repeating patterns Many natural phenomena are textures...
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What is Texture?
Texture depicts spatially repeating patterns
Many natural phenomena are textures
radishes rocks yogurt
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Texton Discrimination (Julesz)
Human vision is sensitive to the difference of some types of elements and appears to be “numb” on other types of differences.
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Search Experiment I
The subject is told to detect a target element in a number of background elements.In this example, the detection time is independent of the number of background elements.
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Search Experiment II
In this example, the detection time is proportional to the number of background elements,And thus suggests that the subject is doing element-by-element scrutiny.
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Heuristic (Axiom) I
Julesz then conjectured the following axiom:
Human vision operates in two distinct modes: 1. Preattentive vision parallel, instantaneous (~100--200ms), without scrutiny, independent of the number of patterns, covering a large visual field. 2. Attentive vision serial search by focal attention in 50ms steps limited to small aperture.
Then what are the basic elements?
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Heuristic (Axiom) II
Julesz’s second heuristic answers this question:
Textons are the fundamental elements in preattentive vision, including 1. Elongated blobs rectangles, ellipses, line segments with attributes color, orientation, width, length, flicker rate. 2. Terminators ends of line segments. 3. Crossings of line segments.
But it is worth noting that Julesz’s conclusions are largely based by ensemble of artificial texture patterns. It was infeasible to synthesize natural textures for controlled experiments at that time.
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Examples
Pre-attentive vision is sensitive to size/width, orientation changes
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Examples
Sensitive to number of terminators
Left: fore-backRight: back-fore
See previous examples For cross and terminators
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Julesz Conjecture
Textures cannot be spontaneously discriminated if they have the same first-order and second-order statistics and differ only in their third-order or higher-order statistics.
(later proved wrong)
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1st Order Statistics
5% white 20% white
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2nd Order Statistics
10% white
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Capturing the “essence” of texture
…for real images
We don’t want an actual texture realization, we want a texture invariant
What are the tools for capturing statistical properties of some signal?
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Multi-scale filter decomposition
Filter bank
Input image
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Filter response histograms
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Heeger & Bergen ‘95
Start with a noise image as output Main loop:
• Match pixel histogram of output image to input
• Decompose input and output images using multi-scale filter bank (Steerable Pyramid)
• Match subband histograms of input and output pyramids
• Reconstruct input and output images (collapse the pyramids)
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Image Histograms
Cumulative Histograms
s = T(r)
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Histogram Equalization
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Histogram Matching
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Match-histogram code
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Image Pyramids
Known as a Gaussian Pyramid [Burt and Adelson, 1983]• In computer graphics, a mip map [Williams, 1983]• A precursor to wavelet transform
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Band-pass filtering
Laplacian Pyramid (subband images)Created from Gaussian pyramid by subtraction
Gaussian Pyramid (low-pass images)
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Laplacian Pyramid
How can we reconstruct (collapse) this pyramid into the original image?
Need this!
Originalimage
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Steerable PyramidInput image
7 filters used:
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Heeger & Bergen ‘95
Start with a noise image as output Main loop:
• Match pixel histogram of output image to input
• Decompose input and output images using multi-scale filter bank (Steerable Pyramid)
• Match subband histograms of input and output pyramids
• Reconstruct input and output images (collapse the pyramids)
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Simoncelli & Portilla ’98+
Marginal statistics are not enough
Neighboring filter responses are highly correlated • an edge at low-res will cause an edge at high-res
Let’s match 2nd order statistics too!
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Simoncelli & Portilla ’98+
Match joint histograms of pairs of filter responses at adjacent spatial locations, orientations, and scales.
Optimize using repeated projections onto statistical constraint sufraces
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Texture for object recognition
A “jet”
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ObjectObject Bag of ‘words’Bag of ‘words’
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Analogy to documentsAnalogy to documents
Of all the sensory impressions proceeding to the brain, the visual experiences are the dominant ones. Our perception of the world around us is based essentially on the messages that reach the brain from our eyes. For a long time it was thought that the retinal image was transmitted point by point to visual centers in the brain; the cerebral cortex was a movie screen, so to speak, upon which the image in the eye was projected. Through the discoveries of Hubel and Wiesel we now know that behind the origin of the visual perception in the brain there is a considerably more complicated course of events. By following the visual impulses along their path to the various cell layers of the optical cortex, Hubel and Wiesel have been able to demonstrate that the message about the image falling on the retina undergoes a step-wise analysis in a system of nerve cells stored in columns. In this system each cell has its specific function and is responsible for a specific detail in the pattern of the retinal image.
sensory, brain, visual, perception,
retinal, cerebral cortex,eye, cell, optical
nerve, imageHubel, Wiesel
China is forecasting a trade surplus of $90bn (£51bn) to $100bn this year, a threefold increase on 2004's $32bn. The Commerce Ministry said the surplus would be created by a predicted 30% jump in exports to $750bn, compared with a 18% rise in imports to $660bn. The figures are likely to further annoy the US, which has long argued that China's exports are unfairly helped by a deliberately undervalued yuan. Beijing agrees the surplus is too high, but says the yuan is only one factor. Bank of China governor Zhou Xiaochuan said the country also needed to do more to boost domestic demand so more goods stayed within the country. China increased the value of the yuan against the dollar by 2.1% in July and permitted it to trade within a narrow band, but the US wants the yuan to be allowed to trade freely. However, Beijing has made it clear that it will take its time and tread carefully before allowing the yuan to rise further in value.
China, trade, surplus, commerce,
exports, imports, US, yuan, bank, domestic,
foreign, increase, trade, value
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categorycategorydecisiondecision
learninglearning
feature detection& representation
codewords dictionarycodewords dictionary
image representation
category modelscategory models(and/or) classifiers(and/or) classifiers
recognitionrecognition
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1.Feature detection and representation1.Feature detection and representation
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Feature detectionFeature detection
• Sliding Window– Leung et al, 1999– Viola et al, 1999– Renninger et al 2002
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Feature detectionFeature detection
• Sliding Window– Leung et al, 1999– Viola et al, 1999– Renninger et al 2002
• Regular grid– Vogel et al. 2003– Fei-Fei et al. 2005
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Feature detectionFeature detection
• Sliding Window– Leung et al, 1999– Viola et al, 1999– Renninger et al 2002
• Regular grid– Vogel et al. 2003– Fei-Fei et al. 2005
• Interest point detector– Csurka et al. 2004– Fei-Fei et al. 2005– Sivic et al. 2005
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Feature detectionFeature detection
• Sliding Window– Leung et al, 1999– Viola et al, 1999– Renninger et al 2002
• Regular grid– Vogel et al. 2003– Fei-Fei et al. 2005
• Interest point detector– Csurka et al. 2004– Fei-Fei et al. 2005– Sivic et al. 2005
• Other methods– Random sampling (Ullman et al. 2002)– Segmentation based patches
• Barnard et al. 2003, Russell et al 2006, etc.)
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Feature RepresentationFeature Representation
Visual words, aka textons, aka keypoints:
K-means clustered pieces of the image
• Various Representations:– Filter bank responses– Image Patches– SIFT descriptors
All encode more-or-less the same thing…
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Interest Point FeaturesInterest Point Features
Normalize patch
Detect patches[Mikojaczyk and Schmid ’02]
[Matas et al. ’02]
[Sivic et al. ’03]
Compute SIFT
descriptor
[Lowe’99]
Slide credit: Josef Sivic
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…
Interest Point FeaturesInterest Point Features
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…
Patch FeaturesPatch Features
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dictionary formationdictionary formation
…
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Clustering (usually k-means)Clustering (usually k-means)
Vector quantization
…
Slide credit: Josef Sivic
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Clustered Image PatchesClustered Image Patches
Fei-Fei et al. 2005
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Filterbank
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Textons (Malik et al, IJCV 2001)
• K-means on vectors of filter responses
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Textons (cont.)Textons (cont.)
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Image patch examples of codewordsImage patch examples of codewords
Sivic et al. 2005
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Visual Polysemy. Single visual word occurring on different (but locally similar) parts on different object categories.
Visual Synonyms. Two different visual words representing a similar part of an object (wheel of a motorbike).
Visual synonyms and polysemy
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Image representationImage representation
…..
fre
que
ncy
codewords
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Vision Science &Computer Vision Groups
University of California Berkeley
Scene Classification (Renninger & Malik)
kitchen livingroom bedroom bathroom
city street farm
beach mountain forest
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Vision Science &Computer Vision Groups
University of California Berkeley
kNN Texton Matching
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Vision Science &Computer Vision Groups
University of California Berkeley
0
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street
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bathroom city
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Discrimination of Basic Categories
texture model
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Vision Science &Computer Vision Groups
University of California Berkeley
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Discrimination of Basic Categories
texture model
chance
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Vision Science &Computer Vision Groups
University of California Berkeley
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Discrimination of Basic Categories
texture model
chance
37 ms
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Vision Science &Computer Vision Groups
University of California Berkeley
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bathroom city
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Discrimination of Basic Categories
texture model
chance
50 ms
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Vision Science &Computer Vision Groups
University of California Berkeley
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% c
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street
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bathroom city
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livingroom
Discrimination of Basic Categories
texture model
chance
69 ms
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Vision Science &Computer Vision Groups
University of California Berkeley
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street
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bathroom city
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Discrimination of Basic Categories
texture model
chance
37 ms 50 ms 69 ms
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Object Recognition using texture
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Learn texture model representation:
• Textons (rotation-variant)
Clustering• K=2000
• Then clever merging
• Then fitting histogram with Gaussian
Training• Labeled class data