Friday, July 7, 2017

Image retrieval using segmentation project for ECE

With the exponential growth in image databases available on the Internet, we need an efficient storage, cataloging and retrieval system for images. Images in a database are typically indexed using text annotation, which is dependent upon the language and point of view of the operator. Content based image retrieval (CBIR), in contrast, is the method of retrieving images similar to a given query image using only the content of the image. Many features such as color, texture, and shape represent the content of the image can.
There are many algorithms for image retrieval. However, most of them are based on the global features of the images. One disadvantage of global feature-based image retrieval is that a user is often interested only in a single object or in a few objects in the image, whereas a global

mechanism uses all features for retrieval, including the background features. In order to overcome his shortcoming of the global feature-based image retrieval, a new image retrieval algorithm based on local features is proposed.
The new algorithm exploits image segments in the retrieval process. In segment-based image retrieval, a given image is divided into homogeneous regions and retrieval is based on a subset of the objects present in the image. Automated segmentation is a very powerful tool in retrieval because the user can narrow the field of search by selecting the objects in the image. In this paper we attempt to provide a segmentation algorithm and retrieve images based on the component features. Color,  texture and shape of the component are extracted and stored in a database for use in CBIR. Figure 1 provides a flow chart for the algorithm. The images in the library are segmented and features are extracted off-line. When retrieval is initiated, the query segments are matched with segments from the feature database and the retrieved images are displayed.

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