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Object-based Image Representation

This research focuses on developing an object-based image representation scheme aimed at improving the accessibility and efficiency of large-scale image databases. By concentrating on identified objects within images, this method enables faster image querying, reduction in storage requirements, and enhanced analysis of spatial-temporal relationships. The study explores object extraction, description, and the application of time-series coding, while outlining future research directions such as exploring relationships between maps and images and refining segmentation techniques.

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Object-based Image Representation

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  1. Object-based Image Representation Dr. B.S. Manjunath Sitaram Bhagavathy Shawn Newsam Baris Sumengen Vision Research Lab University of California, Santa Barbara

  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 Query user 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

  6. Objects from the image An aerial image Examples of objects

  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 Object-based Image Representation

  9. object object RGB to LUV conversion 24 Gabor filters 24-dim outputs K-means clustering K-means clustering Take means of clusters Take means and percentages Convert means to RGB Dominant texture feature Dominant color feature

  10. Time-series object coding Objective: To apply object-based video coding (based on MPEG-4) techniques for coding time-series of objects.

  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 Object-based Image Representation

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