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Object-based Image Representation
Dr. B.S. Manjunath
Sitaram Bhagavathy
Shawn Newsam
Baris Sumengen
Vision Research Lab
University of California, Santa Barbara
Object-based Image Representation 2
Outline
• Context and Objective
• Introduction
• Object Extraction and Description
• Time series object coding
• Future research ideas
• Conclusion
Object-based Image Representation 3
Context
Large-scale Image Database
user
Query
Retrieved images
Query example: “Give me all images similar to image X.”
Object-based Image Representation 4
Objective
To develop an object-based image representation
scheme in order to facilitate the following:
• Faster access of data in the context of object-based querying
• Reducing required storage space for images
• Relating maps and geographical aerial images
• Study of spatio-temporal relationships in/among aerial images
Note: Although our dataset consists of aerial images, we expect the scheme to be useful for other image datasets as well.
Object-based Image Representation 5
The Object-based Approach
Assumptions:
• Useful information (for searching) in images is concentrated in smaller regions termed objects.
• Objects are mostly homogeneous in color and texture and can be characterized thus.
• Most queries on image databases are in terms of objects; e.g. “Give me all images having a brown field.”
Object-based Image Representation 7
Why Object-based approach?
• Uses semantic information for querying (user friendly)
• Efficient description of images– We ignore portions of images that would not be used for querying
• Potential reduction of storage space– Store images as collections of objects– Redundancy removal in time-series of objects
Object-based Image Representation 8
Past Research
• Object Extraction: Identify and extract semantic objects from aerial images. – So far, we have done this manually
– Working on automatic segmentation using semantic models (Sumengen)
• Object Description: Find efficient descriptors for the objects (Bhagavathy)– Shape: binary alpha plane
– Dominant Colors (Deng, Manjunath)
– Dominant textures
9
RGB to LUV conversion
Take means of clusters
K-means clustering
Convert means to RGB
object
Dominant color feature
24 Gabor filters
K-means clustering
Take means and
percentages
object
Dominant texture feature
24-dim outputs
10
Time-series object codingObjective: To apply object-based video coding (based on MPEG-4) techniques for coding time-series of objects.
Object-based Image Representation 11
Future Research Possibilities
• Storage space issues
– describe image as a collection of objects
– reconstruct image from its objects
• Relation between maps and aerial images
– maps have information, images have data
• Spatial and temporal relationships among objects
– variation of objects with time
– spatial querying (Newsam)
• Application in wireless networks
Object-based Image Representation 12
Conclusion
• The object-based approach enables semantic querying which is more user-friendly
• Time-series compression of objects reduces required storage space for large images
• Potential to reduce required bandwidth for wireless transmission of image information
• Enables the study of temporal change in images
• Enables spatial querying