Content based image retrieval(cbir)
- 1. KUVEMPU UNIVERSITY Dept. of MCA & Computer Science Jnana Sahyadri, Shankaraghatta ---By Prasad Babu M.Sc. CS 3 rd Semester Dept. of MCA & Computer Science Jnana Sahyadri, Shankaraghatta ---Under the Guidance of Ravi Kumar. M Associate Professor Dept. of MCA & Computer Science Jnana Sahyadri, Shankaraghatta Seminar on : Content Based Image Retrieval(CBIR)
- 2. Contents: History of CBIR Introduction Challenges CBIR Techniques Color Image Variances CBIR Model & its working Applications of CBIR Limitations of CBIR Conclusion References
- 3. History: The term CBIR seems to have originated in 1992, when it was used by T. Kato to describe experiments into automatic retrieval of images from a database, based on the colors and shapes present. Since then, the term has been used to describe the process of retrieving desired images from a large collection on the basis of image features. The techniques, tools and algorithms that are used originated from fields such as statistics, pattern recognition.
- 4. Introduction: Why CBIR???? Digital image database growing rapidly in size Professional needs – Logo Search Difficulty in locating images on the web Example Find a picture of Tom & Jerry in Set of Cartoons…..
- 5. Content Based Image Retrieval Content-based image retrieval (CBIR) is the application of computer vision to the image retrieval problem , that is, the problem of searching for digital images in large databases. "Content-based" means that the search will analyze the actual contents of the image. The term 'content' in this context might refer colors, shapes, textures, or any other information that can be derived form the image itself.
- 6. Challenges: Semantic gap The semantic gap is the lack of coincidence between the information that one can extract from the visual data . User seeks semantic similarity, but the database can only provide similarity by data processing. Huge amount of objects to search among.
- 7. How to search images????? Color Local Shape Texture
- 8. Color: Color similarity is achieved by computing a color histogram for each image that identifies the proportion of pixels within an image holding specific values (that humans express as colors). Examining images based on the colors they contain is one of the most widely used techniques because it does not depend on image size or orientation. Color searches will usually involve comparing color histograms , though this is not the only technique in practice.
- 9. Shape: Shape does not refer to the shape of an image but to the shape of a particular region that is being sought out. Shapes will often be determined first applying segmentation or edge detection to an image. Other methods like use shape filters to identify given shapes of an image.
- 10. Texture: Texture measures look for visual patterns in images and how they are spatially defined. These sets not only define the texture, but also where in the image the texture is located. Texture is a difficult concept to represent. The identification of specific textures in an image is achieved primarily by modeling texture as a two-dimensional gray level variation.
- 11. Color Images: Problems with color variances Surface Orientation Camera Viewpoint Intensity of the Light
- 12. CBIR Model: Fig: Block Diagram of CBIR System
- 14. Applications of CBIR: Search for one specific image. General browsing to make an interactive choice. Search for a picture to go with a broad story or search to illustrate a document.
- 15. Limitations: We do not yet have a universally acceptable algorithmic means of characterizing human vision, more specifically in the context of image understanding. Hence it is not surprising to see continuing efforts towards it, either building up on prior work or exploring novel directions.
- 16. Conclusion: CBIR is used to search a specific image from a large database… CBIR makes interactive search of images from the database… At present this technique is implemented by Google…
- 17. References: