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Content-based Image Retrieval. Presentation by Charlie Neo. Introduction. Why Digital image database growing rapidly in size Professional needs – Logo Search Difficulty in locating images on the web Example Find a picture of me and jack on the bus. Application CBIR.

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content based image retrieval

Content-based Image Retrieval

Presentation by

Charlie Neo

introduction
Introduction
  • Why
    • Digital image database growing rapidly in size
    • Professional needs – Logo Search
    • Difficulty in locating images on the web
  • Example
    • Find a picture of me and jack on the bus
application cbir
Application CBIR
  • Search for one specific image.
  • General browsing to make an interactive choice.
  • Search for a picture to go with a broad story or search to illustrate a document.
  • Search base on the esthetic value of the picture.
two classes of cbir narrow vs broad domain
Two Classes of CBIRNarrow vs. Broad Domain
  • Narrow
    • Medical Imagery Retrieval
    • Finger Print Retrieval
    • Satellite Imagery Retrieval
  • Broad
    • Photo Collections
    • Internet
challenges
Challenges
  • Semantic gap
    • The semantic gap is the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation.
    • User seeks semantic similarity, but the database can only provide similarity by data processing.
  • Huge amount of objects to search among.
  • Incomplete query specification.
  • Incomplete image description.
color image processing
Color Image Processing
  • Problems with color variances
    • Surface Orientation
    • Camera Viewpoint
    • Position of Illumination
    • Intensity of the Light
color image processing9
Color Image Processing
  • Approaches
    • Opponent color axes
      • Advantage of isolating the brightness information on the third axis.
      • Invariant to changes in illumination intensity and shadows.
    • HSV-representation
      • Invariant under the orientation of the object with respect to the illumination and camera direction.
    • Search for clusters in a color histogram to identify which pixels in the image originate from one uniformly colored object.
image processing for local shape
Image Processing for Local Shape
  • Problems
    • Occlusion
    • Different Viewpoint
  • Approaches
    • Collect all properties that capture geometric details in the image.
    • Invariant Descriptors.
image texture processing
Image Texture Processing
  • Problems
    • Offer little semantic referent.
  • Approaches
    • Markovian analysis
    • Wavelets
      • Generated by groups of dilations or dilations and rotations
      • Some semantic correspondent.
  • Great For
    • Satellite images
    • Images of documents
description of content using features
Description Of Content using Features
  • Grouping Data
    • Strong Segmentation
      • Region T = 0 (object)
      • Shape and Object features
    • Weak Segmentation
      • T subset of 0
      • Salient features
    • Sign Detection
      • Signs Probabilities
    • Partitioning
      • Global feature
global and accumulating features
Global and Accumulating Features
  • Accumulation of features using a histogram
  • The set of features F(m) ordered by histogram index m.
  • 64-bin histogram, has discriminating power up to 25,000 images
salient features
Salient Features
  • Weak segmentation. (grouping of data into homogeneous regions.)
  • Salient feature calculations lead to sets of regions or points with known location and feature values.
  • Innovation of content-based retrieval.
  • Expected to receive much attention in the further expansion of the field.
signs
Signs
  • Typical signs are an icon, a character, a traffic light, or a trademark.
  • Strong semantic interpretation is within grasp
  • Analysis tends to become application-oriented.
shape and object features
Shape and Object Features
  • Object segmentation is hard.
    • Possible for narrow domain.
  • Fortunately, for our purpose it only requires detection of the object’s presents.
description of structure and lay out
Description of Structure and Lay-Out
  • Structural feature
    • Feature values
    • Relationships between object sets.
    • captured in a graph
  • Lay-out descriptions
    • characterized by locations, size, and features.
interpretation and similarity
Interpretation And Similarity
  • Semantic Interpretation
    • Derive interpretation from feature set.
    • Features generate a probability distribution.
    • MAVIS2-system: four semantic layers.
  • Similarity
    • Similarity measure Sq,d between the images q and d
    • Sq,d = s(Fq,Fd).
    • s(Fq,Fd) = g( d(Fq,Fd) )
    • e.g. dcan be just the Euclidean distance.
user interaction
User Interaction
  • Query Space: Definition and Initialization
    • Q = {IQ,FQ,SQ,ZQ}
    • IQ is a selection of images from the large image archive I.
    • FQ is a selection of features from feature set F.
    • SQ similarity function.
    • ZQ is a set of labels to capture goal dependent semantics.
query space display
Query Space Display
  • Besides just showing the images that match the query …
  • Images are placed in such a way that distances between images in the display reflect SQ.
  • Highlight parts indicating which parts of the image fulfill the criteria. (exact query)
interacting with query space
Interacting with Query Space
  • The process of query specification and display is iterated, where, in each step, the user revises the query.
  • user feedback leads to an update of query space:
  • Both positive and negative examples is used.
  • Each iteration, the probability of being the target for an image in IQ is increased or decreased
system aspects storage and indexing
SYSTEM ASPECTS:Storage and Indexing
  • Standard, Linear File System
    • O(N)
  • Three classes of indexing methods
    • Space partitioning
    • Data partitioning
    • Distance-based technique
    • Varies tree structure
    • O(log N)
system aspects system architectures
SYSTEM ASPECTS:System Architectures
  • Separate indexing and retrieval.
  • Image retrieval as a plug-in module to an existing database system.
  • Analysis, indexing and training as modules
system aspects system evaluation
SYSTEM ASPECTS:System Evaluation
  • Relevance is subjective.
  • Human subjects to produce idea ordering for a query.
cortina a system for large scale content based web image retrieval
Cortina: A System for large scale, content based Web Image Retrieval
  • Built for Web Image Retrieval
  • 3 million images
  • Clusters
  • Data Mining for Semantics
cortina a system for large scale content based web image retrieval29
Cortina: A System for large scale, content based Web Image Retrieval
  • Four global feature descriptor
    • Homogeneous Texture Descriptor
    • Edge Histogram Descriptor
    • Scalable Color Descriptor
    • Dominant Color Descriptor
  • Linear combination of the 4 features, as the distance for K-NN search.
conclusion
Conclusion
  • There is a need for CBIR
  • Image retrieval does not entail solving the general image understanding problem. It may be sufficient that a retrieval system present similar images, similar in some user-defined sense.
  • Interaction
  • The need for database
  • The semantic gap