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Transcript of Image and video descriptors Advanced Topics in Computer Vision Spring 2010 Weizmann Institute of...
Image and video descriptors
Advanced Topics in Computer Vision
Spring 2010
Weizmann Institute of Science
Oded Shahar and Gil Levi
Outline
• Overview
• Image Descriptors– Histograms of Oriented Gradients Descriptors– Shape Descriptors– Color Descriptors
• Video Descriptors
Overview - Motivation
• The problem we are trying to solve is image similarity.
• Given two images (or image regions) – are they similar or not ?
Overview - Motivation
• Solution: Image Descriptors.
• An image descriptors “describes” a region in an image.
• To compare two such regions we will compare their descriptors.
Overview - Descriptor
Descriptor FunctionSimilar? Similar?
To compare two images, we will compare their descriptors
Overview - Similarity
• But what is similar to you ?
• Depends on the application !
Overview
• Image (or region) similarity is used in many CV applications, for example:
– Object recognition– Scene classification– Image registration– Image retrieval– Robot localization– Template matching – Building panorama– And many more…
Overview
• Example – 3D reconstruction from stereo images.
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• Comparing the pixels as they are, will not work!
Overview
• Descriptors provide a means for comparing images or image regions.
• Descriptors allow certain differences between the regions – scale, rotation, illumination changes, noise, shape, etc.
Overview - Motivation
Descriptor Function
Similar ? Similar ?
•Again, can’t take the pixels alone…
OverviewComonly used as follows
1. Extract features from the image as small regions
2. Describe each region using a feature descriptor
3. Use the descriptors in application (comparison, training a classifier, etc.)
Overview
• Main problems
– Features Detection – Where to compute the descriptors? will cover briefly
– Feature Description (Descriptors) How to compute descriptors? today
– Feature Comparison How to compare two descriptors? will cover briefly
Overview - Features DetectionDetection MethodsWhere to compute the descriptors?
• Grid
• Key-Points
• Global
Key-Points as Detector Output
• Can be– Points– Regions (of different
orientation, scale and affine trans.)
• Squares • Ellipses• Circles• Etc..
Overview - Features Detection
Overview – Descriptor Comparison
Given two region description, how to compare them?
• Usually descriptor come with it’s own distance function
• Many descriptors use L2 distance
Overview – Descriptor Invariance
• Different applications require different invariance therefore require different descriptors
Similar ?
• Different descriptors measure different similarity• Descriptors can have invariance for visual
effects– Illumination– Noise– Colors– Texture
Outline
• Overview
• Image Descriptors– Histograms of Oriented Gradients Descriptors– Shape Descriptors– Color Descriptors
• Video Descriptors
Descriptor
Descriptor FunctionSimilar? Similar?
To compare two images, we will compare their descriptors
Descriptors
Types of descriptors• Intensity based• Histogram• Gradient based• Color Based• Frequency• Shape• Combination of the above
Descriptors
Why not use patches?
• Very large representation.• Not invariant to small deformations in the descriptor location.• Not invariant to changes in illumination.
Descriptors
Intensity Histogram
0 255
- Not invariant to light intensity change
- Does not capture geometric information
Descriptors
Histogram of image gradients
• Does not capture geometric information
• Normalize for light intensity invariance
Descriptors
Solution: • Divide the area
• For each section compute it’s own histogram
SIFT - David Lowe 1999
Descriptors - SIFT
Input: an image and a location to compute the descriptor
Step 1: Warp the image to the correct orientation and scale, and than extract the feature as 16x16 pixels
16 x 16
How to compute SIFT descriptor
Descriptors - SIFT
Step 2: Compute the gradient for each pixel (direction and magnitude)
16 x 16
Step 3: Divide the pixels into 16, 4x4 squares
Descriptors - SIFT
Step 4: For each square, compute gradient direction histogram over 8 directions.
The result: 128 dimensions feature vector.
Descriptors - SIFT
• Warp the feature into 16x16 square.• Divide into 16, 4x4 squares.• For each square, compute an histogram of the gradient
directions.
=> Feature vector (128)
Descriptors - SIFT
• Use L2 distance to compare features
Can use other distance functions• X^2 (chi square)• Earth mover’s distance
• Weighted by magnitude and Gaussian window ( σ is half the window size)
• Normalize the feature to unit vector
Descriptors - SIFT
Invariance to shift and rotation• Histograms does not contains any geometric
information
• Using 16 histograms allows to preserve geometric information.
Invariance to illumination
• Gradient are invariant to Light intensity shift (i.e. add a scalar to all the pixels)
• Normalization to unit length add invariance to light intensity change (i.e. multiply all the pixels by a scalar)
Descriptors - GLOH
C. S. Krystian Mikolajczyk. A performance evaluation of local descriptors. TPAMI 2005
• Similar to SIFT• Divide the feature into log-polar bins instead of dividing
the feature into square. – 17 log-polar location bins– 16 orientation bins– We get 17x16=272 dimensions.
Analyze the 17x16=272 DimensionsApply PCA analysis, keep 128 components
SURF
• Use integral images to detect and describe SIFT like features
• SURF describes image faster than SIFT by 3 times
• SURF is not as well as SIFT on invariance to illumination change and viewpoint change
Descriptors
Histograms of Oriented Gradients Descriptors
SIFT David Lowe 1999
GLOH Mikolajczyk K., Schmid C 2005
SURF Bay H., Ess A., Tuytelaars T., Van Gool L 2008
Outline
• Overview
• Image Descriptors– Histograms of Oriented Gradients Descriptors– Shape Descriptors– Color Descriptors
• Video Descriptors
Descriptors
Descriptors - Shape Context
Assume we have a good edge detector
Take a patch of edges?Not invariant to small deformations in the shape
=?
Descriptors - Shape Context
• Quantize the edges surface using a log-polar binning• In each bin, sum the number of edge points
Descriptors - Shape Context
Descriptors - Shape Context
Complex Notion of Similarity
Image descriptor
Correlation surface
Input image
The Local Self-Similarity Descriptor
SSD e
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The Local Self-Similarity Descriptor
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Edges
The Local Self-Similarity Descriptor
Properties & Benefits:
1. A unified treatment of repetitive patterns, color, texture, edges
2. Captures the shape of a local region
3. Invariant to appearance
4. Accounts for small local affine & non-rigid deformations
Image descriptor
Correlation surface
Input image
MAX
Color Texture
Template image:
Shape DescriptorsAllows measuring of shape similarity
Shape Context Belongie S., Malik J., Puzicha J. Shape Matching and Object Recognition Using Shape Contexts. PAMI, 2002.
Local Self-Similarity Shechtman E., Irani M. Matching Local Self-Similarities across Images and Videos. CVPR, 2007.
Geometric Blurrg A. C., Malik J. Geometric Blur for Template Matching. CVPR, 2001.
Outperform the commonly used SIFT in object classification task
Horster E., Greif T., Lienhart R., Slaney M. Comparing local feature descriptors in pLSA-based image models.
Descriptors
Outline
• Overview
• Image Descriptors– Histograms of Oriented Gradients Descriptors– Shape Descriptors– Color Descriptors
• Video Descriptors
Color Descriptors
Color Descriptors
Color spaces
• RGB
• HSV
• Opponent
Color Descriptors
Opponent color space
• color information is represented by channel O1 and O2
• O1 and O2 are invariant to offset
• intensity information is represented by channel O3
Color Descriptors
• RGB color histogram
• Opponent O1, O2
• Color moments• Use all generalized color moments up to the second
degree and the first order.• Gives information on the distribution of the colors.
Color Descriptors• RGB-SIFT descriptors are computed for every RGB channel
independently– Normalize each channel separately – Invariant to light color change
• rg-SIFT - SIFT descriptors over to r and g channels of the normalized-RGB space (2x128 dimensions per descriptor)
• OpponentSIFT - describes all the channels in the opponent color space
• C-SIFT - Use O1/O3 and O2/O3 of the opponent color space (2x128 dimensions per descriptor)– Scale-invariant with respect to light intensity.– Due to the definition of the color space, the offset does not cancel
out when taking the derivativeG. J. Burghouts and J. M. Geusebroek
Performance evaluation of local color invariants 2009
Color Descriptors
Light intensity changeLight color change
Light intensity shiftLight color change and shift
Light intensity shift and change
Studies the invariance properties and the distinctiveness of color descriptors
Color Descriptors
Color Descriptors
Color Descriptors
Increased invariance can reduce discriminative power
Color Descriptors
Descriptor performance on image benchmark
Color Descriptors
Descriptors
How to chose your descriptor?
What is the similarity that you need for your application?
Descriptors
DescriptorsNameCapture
SIFTGradient histogramsTexture, gradients
GLOHVariant of SIFT, log-polar descriptorTexture, gradients
SURFFaster variant of SIFT with lower performance
Texture, gradients
Shape Context
Histogram of edges, good for shapes description
Shape, edges
Self-Similarity
Higher level shape description, Invariant to appearance
Shape
RGB-SIFTSIFT descriptors are computed for every RGB channel independently
Texture, gradients
C-SIFTSIFT base on the opponent color space, shown to be better then SIFT for object and scene recognition
Texture, gradients, color
Outline
• Overview
• Image Descriptors– Histograms of Oriented Gradients Descriptors– Shape Descriptors– Color Descriptors
• Video Descriptors
Video Descriptors
Application: Action recognition
Video: More then just a sequence of images
Want to capture temporal information
Video Descriptors
• Space-Time SIFT
P. Scovanner, S. Ali, M. Shah A 3-dimensional sift descriptor and its application to action recognition - 2007
64-directions histogram
Video Descriptors
Actions as Space-Time Shapes
3D Shape Context
M. Grundmann, F. Meier, and I. Essa (2008) “3D Shape Context and Distance Transform for Action Recognition”
Represent an action in a video sequence by a 3D point cloud extracted by sampling 2D silhouettes over time
Input video
Videodescriptor
Correlationvolume
x
y
time
space-time space-time
regionregion
space-time space-time patchpatch
Action detection
The Local Self-Similarity Descriptor in Video
Video Descriptors
• On Space-Time Interest Points; Ivan Laptev– Local image features provide compact and
abstract representations of images, eg: corners
– Extend the concept of a spatial corner detector to a spatio-temporal corner detector
Space-Time Interest Points
• Consider a synthetic sequence of a ball moving towards a wall and colliding with it
• An interest point is detected at the collision point
Space-Time Interest Points• Consider a synthetic sequence of 2 balls moving towards
each other
• Different interest points are calculated at different spatial and temporal scales
coarser scale
Conclusion
• The problem we are trying to solve is similarity between images and videos.
• Descriptors provide a solution
Conclusion
• Tradeoff between preserving information and obtaining invariance.
• Tradeoff between keeping the geometric structure and obtaining invariance properties (perturbations & rotations).
Thank You