Object Based Image Retrieval System Based On Rough Set Theory

image retrieval system based on image objects. In the context of Rough Set Theory we introduce an accurate Object-Based Image Retrieval (OBIR) system that can handle image-based queries, and presents an efficient algorithm to retrieve images from large databases, by defining novel image feature called Object Similarity Ratio used in the proposed system.

This information needs to be accessed by other applications or users. To support this issue, new fields of research have appeared. For instance, the one that involves access to static images in databases is known as image retrieval. Image retrieval systems are defined as those systems that find all images in a given database depicting scenes of some specified type. This type is usually given (pre-selected) by a supervisor or user. These user specifications are known as queries.

Image retrieval systems attempt to search through a database to find images that are perceptually similar to a query image. Image retrieval algorithms roughly belong to two categories: text-based approaches and contentbased methods. Content Based Image Retrieval (CBIR) is an important alternative and complement to traditional text-based image searching and can greatly enhance the accuracy of the information returned. It aims to develop visual-content-based technique to search, browse and retrieve relevant images from large-scale digital image collections. Most proposed CBIR techniques automatically extract low-level features (for example. color, texture, shapes of objects and spatial layout) to measure the similarities among images by comparing the feature differences.

However, we know that there is an obvious semantic gap between what user-queries represent based on the lowlevel image features and what the users think. To overcome the semantic gap, many researchers have investigated techniques that retain some degree of human intervention either during input or search thereby utilizing human semantics, knowledge, and recognition ability effectively for semantic retrieval. These techniques called Object-Based Image Retrieval OBIR .

The proposed system is applied on image database with single centered object images. The expected output is a set of images from the input database each of which contains an object that is most similar to the query image. Object may appear on database images at different locations with varied sizes. For instance, figure (1) shows a query image and the target database images that contain shift, scale, and rotation variations and object of interest encircled by circle. An excellent image retrieval method should be insensitive to these variations .

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