content based image retrieval l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Content-Based Image Retrieval PowerPoint Presentation
Download Presentation
Content-Based Image Retrieval

Loading in 2 Seconds...

play fullscreen
1 / 12

Content-Based Image Retrieval - PowerPoint PPT Presentation


  • 137 Views
  • Uploaded on

Content-Based Image Retrieval. Michele Saad Email: michele.saad@mail.utexas.edu EE-381K-14: Multi-Dimensional Digital Signal Processing March 06, 2008. Motivation. Exponential increase in computing power and electronic storage capacity

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Content-Based Image Retrieval' - bozica


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
content based image retrieval

Content-Based Image Retrieval

Michele Saad

Email: michele.saad@mail.utexas.edu

EE-381K-14: Multi-Dimensional Digital Signal Processing

March 06, 2008

motivation
Motivation
  • Exponential increase in computing power and electronic storage capacity
  • Exponential increase in digital image/video database sizes
  • Increased use of image and video:
    • Entertainment
    • Education
    • Commercial purposes
  • Need abstractions for efficient and effective browsing
slide3

Content-Based Image Retrieval System

  • Feature extraction/selection
  • Indexing
  • System Design

Challenge:

Gap between low-level features and high level user semantics

feature extraction
Feature Extraction
  • Primary Features
    • Color
    • Texture
    • Shape
    • Spatial location
  • Feature Selection Methods
    • Relevance feedback (supervised learning)
    • Fuzzy approach
color features
Color Features
  • Conventional color histogram (CCH)
    • Easy computation
    • Does not encode spatial info
    • Does not encode color pixel similarity
  • Fuzzy color histogram (FCH)
    • Considers degree of color similarity between pixels
    • Robust to quantization error
    • Robust to changes in light intensity
  • Color correlogram
    • Easy computation
    • Distills spatial correlation of colors
  • Color-shape based method
    • Includes area and shape info
color features6
Color Features

Key Paper #1

N. R. Howe, D. P. Huttenlocher, “Integrating Color, Texture and Geometry for Image Retrieval”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. II, pp. 239-246, June 2000.

texture features
Texture Features

Key Paper #2

  • Steerable pyramid
    • Basic filters are translation and rotation of a single function
    • Filter is linear combination of basis functions
    • Only for rotation-invariant texture retrieval
  • Contourlet transform
    • Combination of a Laplacian pyramid and directional filter bank
    • Low computational complexity
  • Gabor wavelet
    • Optimally achieves joint resolution in space and spatial frequency
    • Computationally intensive
    • Highest texture retrieval results
  • Complex directional filter bank (CDFB)
    • Retrieval results comparable with Gabor wavelet results
    • Shift Invariant
texture features8
Texture Features

S. Oraintara, T. T. Nguyen, “Using Phase and Magnitude Information of the Complex directional Filter Bank for Texture Image Retrieval”, Proc. IEEE Int. Conf. on Image Processing, vol. 4, pp. 61-64,Oct. 2007

texture features9
Texture Features

S. Oraintara, T. T. Nguyen, “Using Phase and Magnitude Information of the Complex directional Filter Bank for Texture Image Retrieval”, Proc. IEEE Int. Conf. on Image Processing, vol. 4, pp. 61-64,Oct. 2007

feature selection
Feature Selection

Key Paper #3

  • Online feature selection
  • Relevance feedback learning
  • Fuzzy feature contrast model (FFCM)
  • Boosting algorithm
  • Feature contrast model (FCM) psychological similarity between two objects:
project goal
Project Goal
  • Comparison of color, shape and texture feature extraction algorithms
  • Comparison of two feature selection algorithms incorporating relevance feedback.
  • Simulations to be done on an image dataset of 10,000 images from the misc database
references
References
  • [1]. A. Bovik, Handbook of Image and Video Processing, 2nd Edition, Elsevier Academic Press, ISBN 0-12-119792-1, 2005.
  • [2]. J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu and R. Zabih, “Time Indexing Using Color Correlograms”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 762 – 768, June 1997.
  • [3]. S. Oraintara and T. T. Nguyen, “Using Phase and Magnitude Information of the Complex directional Filter Bank for Texture Image Retrieval”, Proc. IEEE Int. Conf. on Image Processing, vol. 4, pp. 61-64,Oct. 2007.
  • [4]. W. Jiang, G. Er, Q. Dai and J. Gu, “Similarity-Based Online Feature Selection in Content-Based Image Retrieval”, IEEE Trans. on Image Processing, vol. 15, no. 3, pp. 101-104, March 2006.
  • [5]. M. Kokare, P.K. Biswas and B.N. Chatterji, “Texture Image Retrieval Using New Rotated Complex Wavelet Filters”, IEEE Trans. on Systems, Man and Cybernetics- Part B: Cybernetics, vol. 23, no. 6, pp. 1168 - 1178, Dec. 2005.
  • [6]. P Liu, K. Jia, Z. Wang and Z. Lv, “A New and Effective Image Retrieval Method Based on Combined Features”, Proc. IEEE Int. Conf. on Image and Graphics, vol. I, pp. 786-790, August 2007.
  • [7]. N. R. Howe and D. P. Huttenlocher, “Integrating Color, Texture and Geometry for Image Retrieval”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. II, pp. 239-246, June 2000.
  • [8]. N. V. Shirahatti and K. Barnard, “Evaluating Image Retrieval”, Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, vol. I, pp. 955-961, June 2005.
  • [9]. S. Deb and Y. Zhang, “An Overview of Content-Based Image Retrieval Techniques”, Proc. IEEE Int. Conf. on Advanced Information Networking and Application, vol. I, pp. 59-64, 2004.