pulkit agrawal y7322 bvv sri raj dutt y7110 sushobhan nayak y7460
TRANSCRIPT
Pulkit AgrawalY7322
BVV Sri Raj DuttY7110
Sushobhan NayakY7460
OutlineWhat is a sceneScene recognitionMethodResultsFuture WorkReferences
What is a Scene?Scene- as opposed to
‘object’ or ‘texture’
Object: when view subtends 1 to 2 meters around observer---hand distance
What is a Scene?
observer and fixated point- >5 meters
Scene Recognition2 approachesObject recognition
Global info – details and object info ignoredo Experimental
evidenceo ‘Gist’ of image
Scene RecognitionExclusive
classificationStructural
attributes- Continuous organization of scenes along semantic axes
Semantic axes2 levels:
Degree of naturalness: man-made to natural landscape
Ambiguous (building in field) pictures around center
Semantic axesNatural scenes-
degree of openness
Artificial urban scenes- degree of verticalness and horizontalness
Highways--Highways +Tall Building---Tall Buildings
Method
Information at various Scales
What do we Need ??
High Frequency ? Low Frequency ?
Both ??
Feature ExtractionImage Power Spectrum
Gabor Filters (Scale, Orientation)
Features (512 used)
Mathematical Details…Important data from Image power spectrum
Structural discriminant feature
DST=Discriminat Spectral Template- --an encoding of the discriminant structure between two image categories
‘u’ -weighted integral of power spectrum
Classification
Image = Feature Vector()
Required Classes
Linear Discriminant Analysis
Discriminating Vector (D.V)Maximum Separation b/w classes
Mathematical Details…..Image represented as Feature Vector x.m1 , m2: mean vector of feature vector of 2
classes
Mathematical Details…
gn= feature
Gn = Gabor filter
dn = through learning
Learning…Projection of Training Set
Image F.V. on D.V.
Use LDA to determine Threshold
Classifier Obtained
Learning
Work..
Artificial v/s Natural
Open v/s Non Open
ResultsArtificial v/s Natural
Artificial•80 Test Images•67 classified Correctly
Natural•80 Test Images•75 classified Correctly
89% Correct results
Result
Future WorkArrangement in semantic axesAddition of features
Depth Symmetry
Contrast Ruggedness
8 category arrangement (skyscrapers, highway, street, flat building, beach, field, mountain, forest)
Experiment with Haar and other filters
ReferenceTorralba A. & Olivia A., Semantic
Organisation of Scenes using Discriminant Structural Templates (1999)
Torralba A. & Olivia A., Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope(2001)
Olivia A., Gist of the Scenehttp://people.csail.mit.edu/torralba/code/spati
alenvelope/