A Scheme for Visual Feature based Image Indexing - PowerPoint PPT Presentation

1 / 13
About This Presentation
Title:

A Scheme for Visual Feature based Image Indexing

Description:

A Scheme for Visual Feature based Image Indexing. HongJiang ... features such as color, texture. Need an effective indexing scheme utilizing these features ... – PowerPoint PPT presentation

Number of Views:37
Avg rating:3.0/5.0
Slides: 14
Provided by: vibhore6
Category:

less

Transcript and Presenter's Notes

Title: A Scheme for Visual Feature based Image Indexing


1
A Scheme for Visual Feature based Image Indexing
  • HongJiang Zhang and Di Zhong
  • SPIE Conf. on Storage and Retrieval for Image and
    Video Databases
  • Feb 1995
  • Presented by Vibhore Vardhan

2
Motivation
  • Digital images need to be manipulated and managed
    as images
  • Retrieve visual data based on visual content of
    image
  • E.g. IBMs QBIC system
  • More emphasis on deriving visual features such as
    color, texture
  • Need an effective indexing scheme utilizing these
    features
  • Necessary to browse large image databases

3
Multi-dimensional Index
  • Pre-computed visual features for each item in
    database
  • Key attribute for an item is a feature vector
  • Search based on similarities between feature
    vectors
  • Three popular approaches to multi-dimensional
    indexing
  • R tree, Linear quadtrees, and Grid files
  • But they assume the following
  • Distance Euclidean distance of points in
    feature space
  • Dimensionality of feature space is low
  • Efficient filter allows false positives, but no
    false dismissals
  • Lower dimensional feature space or narrow search
    space

4
Tree Indexing by Abstraction and Classification
  • For given attribute Aj, identify and label all
    objects that share Aj
  • Identical value or a certain range al
  • Objects with same label are clustered to form an
    abstraction
  • Abstractions are represented as nodes in the
    index tree
  • Apply these abstraction operations recursively to
    reach root node
  • Automate using Self-Organization feature Maps
    (SOM)
  • Unsupervised learning based on a grid of
    artificial neurons
  • Weights are adapted to match input vectors in a
    training set
  • First described by Teuvo Kohonen

5
Architecture of Self-Organization Map
of nodes gt of possible classes
associated weight
image feature vector
  • Two layer SOM mapping from input data in Rn
    onto a 2-d grid
  • All ref. vectors compared with 1 input vector
    according to metrics
  • Select best matching node in the map, update
    neighbors
  • After several iterations, SOM adapts to input

6
Hierarchical SOM
  • Modify SOM to meet certain properties
  • Constructs an index tree which forms similarity
    space of feature data
  • First form bottom level L through learning
  • Each node in L represents a group of image which
    are similar
  • Higher levels are created by applying clustering
    and projection

7
Results Texture Features based Index
Iconic map of Brodatz texture database
8
Results Texture Features based Index
9
Results Texture Features based Index
  • Texture feature set model (20 dimension feature
    vector)
  • Multi-resolution simultaneous auto-regressive
    (MRSAR)
  • Combined MRSAR with coarseness features and gray
    histograms
  • Improves accuracy feature vector size goes up
    to 30
  • Evaluated on Brodatz texture database
  • 112 classes of images (512x512 8-bits)
  • 9 subimages (128x128 8-bits) in each class
  • Retrieval rate number of retrieved subimages
    (same class as query)
  • number of
    retrieval neighbors

10
Results Texture Features based Index
  • MRSAR does as well as global search for 9-nearest
    neighbor searching
  • 5x speed improvement
  • Adding coarseness and histogram improves accuracy
    by 6

11
Results Color Histogram based Index
Color histogram of images as feature vector for
317 images 106 images classified into 7
categories, rest act as noise
Compared indexing using color histograms in 3
color spaces Results for RGB space with 10
neighbors
Retrieval accuracy similar to global search, but
faster LUV color space gave the highest retrieval
rate
12
Conclusion
  • Initial work in developing an effective indexing
    scheme
  • Feature vector for images have high dimensions
  • Not suitable for traditional indexing approaches
  • Implements hierarchical SOM
  • Results show good accuracy and speedups

13
DEMO
  • PicSOM
Write a Comment
User Comments (0)
About PowerShow.com