IMAGE RETRIEVAL: METADATA AND TOOLS

1. IMAGE RETRIEVAL: METADATA AND TOOLS MudasirKhazer HumaShafiq Iram Mahajan TazeemZainab Research Scholars, Department of Library & Information Science University of Kashmir, Hazratbal, Srinagar, 190006. & UzmaQadri Assistant Librarian, AllamaIqbal Library University of Kashmir, Hazratbal Srinagar, 190006.

2. METADATA Metadata is data about data. It is a descriptive information about digital resources. It can be of: • individual files • collections of files • complete projects

3. NEED OF METADATA Digitization is an important aspect in the present tech savvy world but mere act of creating digital copies of collection materials does not make those materials findable, understandable, or utilizable to our ever-expanding audience of online users. Digitization combined with the creation of carefully crafted metadata can significantly enhance end-user access.

4. IMAGE RELATED METADATA Metadata may be related to: • the content of a image • the quality of image • source of image • the digital version of that same image. • the relationship between that image and other image.

5. IMAGE RETRIEVAL SYSTEM • A computer system for browsing, searching and retrieving images from a large database of digital images. • Traditional and common methods of image retrieval utilize some method of adding metadata such as captioning', keywords, or descriptions to the images so that retrieval can be performed. • Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation.

6. AUTOMATIC IMAGE ANNOTATION • Also known as automatic image tagging or linguistic indexing. • The process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image.

7. SEARCH METHODS • Image searchis a specialized data search used to find images. To search for images, a user may provide query terms such as keyword, image file/link, or click on some image, and the system will return images "similar" to the query. Search methods are: • Image meta search • Content-based image retrieval (CBIR) • Image collection exploration

8. IMAGE META SEARCH • Search of images based on associated metadata such as keywords, text, etc. • The most common search engines today offer image search such as Google, Yahoo or Bing.

11. CONTENT-BASED IMAGE RETRIEVAL (CBIR) • Also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR). • "Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. • The term "content" in this context refer to colors, shapes, textures, or any other information that can be derived from the image itself.

12. Why CBIR? • CBIR is desirable because searches that rely purely on metadata are dependent on annotation quality and completeness. • Manually annotating images by entering keywords or metadata in a large database can be time consuming and may not capture the keywords desired to describe the image. • The evaluation of the effectiveness of keyword image search is subjective and has not been well-defined.

13. Image collection exploration • Mechanism to explore large digital image repositories. • Consists of a set of computational methods to represent, summarize, visualize and navigate image repositories in an efficient, effective and intuitive way.

14. HOW IMAGE SEARCH WORKS • The metadata of the image is indexed and stored in a large database and when a search query is performed the image search engine looks up the index, and queries are match with the stored information. The results are presented in order of relevancy. • Some search engines can automatically identify a limited range of visual content, e.g. faces, trees, sky, buildings, flowers, colours etc. This can be used alone, as in content-based image retrieval, or to augment metadata in an image search.

15. When performing a search the user receives a set of thumbnail images, sorted by relevancy. Each thumbnail is a link back to the original web site where that image is located. Using an advanced search option the user can typically adjust the search criteria to fit their own needs, choosing to search only images or animations, color or black and white, and setting preferences on image size.

16. CONCLUSION Problems with traditional methods of image indexing and image retrieval are becoming extensively recognized, and the quest for solutions is progressively becoming active area for research and development. Some signs of the rate of growth can be gained from the number of research articles appearing each year on the topic. It has lead to the rise of interest in techniques for retrieving images on the basis of automatically derived characteristics such as colour, texture and shape. The technology now commonly referred as content Based Image Retrieval (CBIR) has been one of the most intense research areas in the field of computer technology over the last decade. The availability of large and gradually growing amount of image and multimedia, and the development of the internet emphasize the need to create thematic access methods that facilitate more than simple text based queries or requests based on matching exact database fields. Many programs and tools have been developed to frame and execute queries based on visual or image content and to help browsing large multimedia

17. Thank you!