CBIR Synopsis

Content Based Image Retrieval Objective : The objective of Content Based Image Retrieval (CBIR) system is to retrieve images from large datasets based on queries regarding their contents. Proposed CBIR Model : The proposed CBIR framework is shown in Figure 1. The images are kept in a database called Image Database. After preprocessing, images are segmented by using the method Image Segmentation using Color and Texture features. Only the dominant segments are considered for feature extraction namely color histogram features, texture features, and image density features (explained in the subsequent sections). Then a single feature vector is constructed and stored in the feature database.

When a query image is submitted by the user, the same work is done as explained above to get its feature vector. For similarity comparison between the query image and the database image, the Euclidean distance method is used. Using an appropriate threshold, images that are semantically closer are retrieved from the database and displayed as a thumbnail. Need : Digital image database growing rapidly in size Professional needs – Logo Search Difficulty in locating images on the web

CBIR System Architecture :

Benefits of Combined Search : There are three benefits of combined searches that are not made apparent in the results. The first is that combined searches using both text and color return results that are more relevant than for searches based on only text or color alone. The higher relevance does not affect the precision values, since images are only deemed as being either relevant or not relevant, irrespective of how relevant they are. Text searching alone finds images based on the semantic meaning of terms, while color matching alone finds images based on the low level comparison of color distribution. Images returned based on either search are likely to match only the semantic meaning or low level meaning as appropriate to the search type. On the other hand, combined searches return results that match both the semantic meaning of terms and the low level color features. This can result in higher relevance of images returned from a combined search. An example of the higher relevance of images returned from a combined search can be seen for images returned from a search of flowers. A search on the term 'flower' will return images of flowers of any color. A search on a query image of a green and yellow flower is likely to return images of green plants, including flowers and other types, that have yellow features. A search on the term 'flower' combined with the color characteristics of query image featuring a green and yellow flower is much more likely to return images with both characteristics, therefore producing more relevant results to the user's requirements. The second benefit is that a larger set of results is produced for combined searches. Images that match either one or both of the specified search criteria for terms and color matching are retrieved. In almost every case, the user is able to obtain a higher number of relevant images for combined searches than for individual searches alone. The third benefit is the ease of carrying out image search. The user can start a search using a text only query and then use retrieved images as queries in combination of text. Image retrieval is an iterative process whereby a user may refine a search using retrieved relevant images as new queries. This allows the user to narrow down the range of results found, so that a more precise range of images can be found.

Technical Requirements : Hardware: PROCESSOR

: PENTIUM IV 2.6 GHz or above

RAM

: 1 GB DD RAM or above

Software: FRONT END

: Java Runtime Environment 5.0

OPERATING SYSTEM : Windows Xp IDE

: Net Beans 5.5

BACK END

: Apache Derby Database

Resources : Available Resources • Image Retrieval Systems : – GIFT – GNU Image Finding Tool • http://www.gnu.org/sojware/gij/ • Full image retrieval system – FIRE – Flexible Image Retrieval Engine • http://www-i6.informaBk.rwth-aachen.de/~deselaers/?re/ • Research image retrieval system • Developed to allow for easy extension – openCV – computer vision library • http://sourceforge.net/projects/opencvlibrary/ • Implements may image processing operations • E.g. face detection and recognition, feature extraction

Applications : 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. Search base on the esthetic value of the picture.

Market Potential : There is growing interest in CBIR because of the limitations inherent in metadata-based systems, as well as the large range of possible uses for efficient image retrieval. Textual information about images can be easily searched using existing technology, but requires humans to personally describe every image in the database. This is impractical for very large databases, or for images that are generated automatically, e.g. from surveillance cameras. It is also possible to miss images that use different synonyms in their descriptions. Systems based on categorizing images in semantic classes like "cat" as a subclass of "animal" avoid this problem but still face the same scaling issues. Potential uses for CBIR include: • Art collection • Photograph archives • Retail catalogs • Medical records

Limitations : Semantic gap The semantic gap is the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation. User seeks semantic similarity, but the database can only provide similarity by data processing.

Huge amount of objects to search among. Incomplete query specification. Incomplete image description.