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Annotation techniques for Query-By-Concept Approach in Image Retrieval System

This paper discusses the necessity and evolution of image retrieval systems, focusing on the Query-by-Concept (QBC) approach. The introduction of the Automated Sampling-Image Annotation (ASIA) technique leverages a hierarchical structure to organize images based on high-level concepts like 'car' and 'water.' It explores how feature extraction and efficient image querying are key components in the QBC framework. The experimental studies highlight the advantages of a monotonic approach, which speeds up image retrieval by performing matching offline, thus accommodating complex images seamlessly.

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Annotation techniques for Query-By-Concept Approach in Image Retrieval System

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  1. Annotation techniques for Query-By-Concept Approach in Image Retrieval System Rakesh Kamatham Venkata

  2. Introduction • Need for Image Retreival System • Different Approaches • Query-by-Example(QBE) • Query-by-Concept(QBC)

  3. Techniques for QBC • Monotonic Tree • ASIA: Automated Sampling-Image Annotation

  4. Monotonic Approach • Organizes the image in Heirarchial Structure

  5. Components of the system • Image Database • Feature Extraction • Image Querying

  6. Case Studies: • Building

  7. ASIA Technique • Annotation is done by high level concepts such as ‘car’, ‘water’ etc. • Datbase images are matched based on the concept.

  8. ASIA Algorithm

  9. ASIA (contd..)

  10. Experimental Studies

  11. Conclusion • Monotonic approach annotates the images based on a model. The model does not differentiate the real world objects. • AISA approach can annotate the images which are complex. It does the matching offline and is much faster in retrieving the image.

  12. Questions ?

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