Content Based Image Retrieval
- 2. INTRODUCTION: • Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases.
- 3. INTRODUCTION: • Searching or Browsing a database of digital images based on the content of the image itself rather than on information about the image. • User searches database by providing a query image. • Content: Refers colors,shapes,textures or any other information that can derived from image itself.
- 5. TECHNIQUES: QUERY TECHNIQUES: Different implementations of CBIR make use of different types of user queries: What is query? • An image you already have. • A rough sketch you draw. • A symbolic description of what you want. Eg:an image of man and a woman on a beach.
- 6. Query By example • It is a technique that involves providing the CBIR system with an example image that it will then base its search upon. • The algorithms may vary depending on the application, but result images should all share common elements with the provided example
- 7. Options for Providing example: • Preexisting image chosen from random set by the user. • User draws a rough approximation of the image they are looking for.eg: general shapes • This techniques removes the difficulties that can arise when trying to describe images with words.
- 8. RELEVANCE FEEDBACK: • Here user progressively refines the search result by marking images in the results as “relevant” ,“not relevant”, or “neutral” to the search query. • And then repeating the search with the new information.
- 9. OTHER QUERY METHOD: • Other methods include specifying the proportions of colors desired (e.g. “80% red,20% blue”) and searching for images that contain an object given in a query image.
- 11. CONTENT COMPARISON TECHNIQUES: The sections below describe common methods for extracting content from images so that they can be easily compared: COLOR: • Examine the image based on the colour. • Doesn’t depend on image size or orientation. • Based on (histograms, gridded layout, wavelets)
- 12. CONTENT COMPARISON TECHNIQUES TEXTURE: • Texture measures look for visual patterns in images and how they are spatially defined. • Represented by texels which are then placed into a number of sets, depending on how many textures are detected in the image.
- 13. CONTENT COMPARISON TECHNIQUES SHAPE: • It doesn’t refer to shape of an image but to the shape of particular region that is being sought out. • It determined by first applying segmentation or edge detection to an image.
- 14. APPLICATIONS: • Art collections. • Medical Image Databases CT, MRI, Ultrasound, The Visible Human • Scientific Databases e.g. Earth Sciences • General image Collections for Licensing
- 15. THANK YOU…..