Image and video descriptors Advanced Topics in Computer

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 To compare two images, we will compare their descriptors Similar? Descriptor Function Similar?

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 – 3 D reconstruction from stereo images. 3 75 12 80 15 30 39 80 102 103 110 23 150 195 200 196 208 19 • 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 Similar ? Descriptor Function • Again, can’t take the pixels alone… Similar ?

Overview Comonly 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 Detection Methods Where to compute the descriptors? • Grid • Key-Points • Global

Overview - Features Detection Key-Points as Detector Output • Can be – Points – Regions (of different orientation, scale and affine trans. ) • • Squares Ellipses Circles Etc. .

Overview – Descriptor Comparison Given two region description, how to compare them? • • Usually descriptor come with it’s own distance function Many descriptors use L 2 distance

Overview – Descriptor Invariance • Different descriptors measure different similarity • Descriptors can have invariance for visual effects – – Illumination Noise Colors Texture Similar ? • Different applications require different invariance therefore require different descriptors

Outline • Overview • Image Descriptors – Histograms of Oriented Gradients Descriptors – Shape Descriptors – Color Descriptors • Video Descriptors

Descriptor To compare two images, we will compare their descriptors Similar? Descriptor Function Similar?

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 • Normalize for light intensity invariance • Does not capture geometric information

Descriptors Solution: • Divide the area • For each section compute it’s own histogram SIFT - David Lowe 1999

Descriptors - SIFT How to compute SIFT descriptor Input: an image and a location to compute the descriptor 16 x 16 Step 1: Warp the image to the correct orientation and scale, and than extract the feature as 16 x 16 pixels

Descriptors - SIFT Step 2: Compute the gradient for each pixel (direction and magnitude) 16 x 16 Step 3: Divide the pixels into 16, 4 x 4 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 16 x 16 square. • Divide into 16, 4 x 4 squares. • For each square, compute an histogram of the gradient directions. => Feature vector (128)

Descriptors - SIFT • Weighted by magnitude and Gaussian window ( σ is half the window size) • Normalize the feature to unit vector • Use L 2 distance to compare features Can use other distance functions • X^2 (chi square) • Earth mover’s distance

Descriptors - SIFT 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) Invariance to shift and rotation • Histograms does not contains any geometric information • Using 16 histograms allows to preserve geometric information.

Descriptors - GLOH • 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 17 x 16=272 dimensions. Analyze the 17 x 16=272 Dimensions Apply PCA analysis, keep 128 components C. S. Krystian Mikolajczyk. A performance evaluation of local descriptors. TPAMI 2005

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

The Local Self-Similarity Descriptor Input image Correlation surface Image descriptor

The Local Self-Similarity Descriptor 3 2 1 1 2 3 3 2 1

The Local Self-Similarity Descriptor Input image Correlation surface Properties & Benefits: Image descriptor MAX 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 Color Texture Edges

Template image:

Descriptors Shape Descriptors Allows 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 Blur rg 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 p. LSA-based image models.

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 • intensity information is represented by channel O 3 • color information is represented by channel O 1 and O 2 • O 1 and O 2 are invariant to offset

Color Descriptors • RGB color histogram • Opponent O 1, O 2 • 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 (2 x 128 dimensions per descriptor) • Opponent. SIFT - describes all the channels in the opponent color space • C-SIFT - Use O 1/O 3 and O 2/O 3 of the opponent color space (2 x 128 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 derivative G. J. Burghouts and J. M. Geusebroek Performance evaluation of local color invariants 2009

Color Descriptors Studies the invariance properties and the distinctiveness of color descriptors Light intensity change Light color change Light intensity shift Light color change and shift Light intensity shift and change

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

Descriptors Name Capture SIFT Gradient histograms Texture, gradients GLOH Variant of SIFT, log-polar descriptor Texture, gradients SURF Faster 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 Shape to appearance RGB-SIFT descriptors are computed for every Texture, gradients RGB channel independently C-SIFT base on the opponent color space, Texture, gradients, color shown to be better then SIFT for object and scene recognition

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 64 -directions histogram P. Scovanner, S. Ali, M. Shah A 3 -dimensional sift descriptor and its application to action recognition - 2007

Video Descriptors Actions as Space-Time Shapes

3 D Shape Context Represent an action in a video sequence by a 3 D point cloud extracted by sampling 2 D silhouettes over time M. Grundmann, F. Meier, and I. Essa (2008) “ 3 D Shape Context and Distance Transform for Action Recognition”

The Local Self-Similarity Descriptor in Video Input video y Correlation volume space-time patch x e m i t space-time region Action detection Video descriptor

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 coarser scale • Different interest points are calculated at different spatial and temporal scales

Conclusion • The problem we are trying to solve is similarity between images and videos. • Descriptors provide a solution

Conclusion • Tradeoff between keeping the geometric structure and obtaining invariance properties (perturbations & rotations). • Tradeoff between preserving information and obtaining invariance.

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