Image features and properties

1. Image features and properties

2. Image content representation The simplest representation of an image pattern is to list image pixels, one after the other starting from the top left corner. Similar representation can be taken for image windows of any size. The vector representation has very high-dimension.

3. Features for image content representation Image features are meaningful, characterizing patterns of the image: “a distinctivecharacteristic of the data: signifiessomething to somebody”. They may refer to the image itself as a whole or more frequently to local, meaningful, detectable structures or parts of the image Global (holistic) features summarize propertieslikecolor and textureof the whole appearance pattern of the image or image regions or represent meaningful structures that are present in the image, like region boundaries and shapes, edges, lines… (A). Local featuresrefer to properties of special image points and theirsurroundingregionsreferred to asinterestpointsor keypoints (B). Local featuresmakesrecognition more robust to partialocclusions and viewpointchanges. Most of the recognitionapproachestoday use suitablelocalfeatures. (B) (A)

4. Image Features Features are usedfor the purposeofmatching in imagetoimagecomparison and imageretrieval, or toextract more meaningfulcontentfor the purposeofimageunderstanding, semanticannotation and multimedia processing. Tothis end twointerrelatedtasks are offundamentalimportance: Feature detection i.e. the processofextracting/ locatingsuchcharacteristicelementsfromrawimage data. Appropriate algorithms or data transformationsare usedtothis end. Featurerepresentationi.e. definition of suitablefeaturedescriptorsthatcapture the featuresaliency and haveniceproperties of invariance for the purpose of recognition and retrieval. Features are usuallyrepresented in vectorform.

5. Featureinvariance • Whenperforming detection oflocalfeaturesitisimportanttocomputestablemetricwithrespectto (small) variations in position. Repeatabilityoffeaturedetectors, i.e. the frequencywithwhichlocalfeaturesdetected in oneimage are found at a distanceepixelsof the same location in a trasformedimage, isimportantformatching. • More in generalwewill talk aboutinvariance as a fundamental property to matching. We are thereforeinterestedespeciallytoinvariantdescriptorsthatkeepstable under severalconditions. A feature F is invariant to condition K for object x, if it has the value Fx regardless the effect of condition K. • Fundamental invariance are wrtphotometric and geometric transformations.

6. Fundamental transformations • Photometric transformations • Light-object interaction • Affine intensity change ( I  aI+b ) • Geometric transformations • Rotation preserves angles, parallel lines and distances • Similarity (rotation + uniform scale) preserves angles, parallel lines and distance ratios • Affine (non uniform scale dependent on direction)valid for: orthographic camera, locally planar object preserves parallelism • Projective preserves intersection and tangency only

7. Effects of main image transformations Illumination Scale Rotation Affine Full perspective light source light interaction with surface cover camera motion and position