Content based Image Retrieval System with Hybrid Feature Set and Recently Retrieved Image Library

Abstract

Content based image retrieval system is a fast growing research area, where the visual content of a query image is used to search images from large scale image databases. In this proposed an effective system, both the semantically and visually relevant features are used to retrieve the related images. The challenge for the CBIR system is how to efficiently capture the features of the query image for retrieval. In traditional content based retrieval system, the visual content features of the whole query image are used for the retrieval purpose. But in the proposed system, the object wise features of query image are utilized for the effective retrieval. Moreover, an active Recently Retrieved Image Library (RRI Library) is used, which increases the accuracy in each retrieval. An RRI library uses an index system, which maintains the recently retrieved images, and during the retrieval process, the proposed system searches the pertinent images from both the database as well as the RRI library and hence the retrieval precision is gradually increased in each retrieval. The proposed CBIR method is evaluated by querying diverse images and the retrieval efficacy is analyzed by calculating the precision-recall values for the retrieval results.

Early Detection of High Blood Pressure and Diabetic Retinopathy on Retinal Fundus Images Using CBRIR Based on Lifting Wavelets

“…Step 4: The distance between ID and IQ can be defined as: (5) Where α j is the weight for region Rj in image ID, and also we use it as the f j31 just as for the query image regions. In Figure 2, a line from a query region to a DB region corresponds to the minimum distance from the region in image IQ (for example with 7 regions) to the region in database image ID (with 9 regions).…”

“…Proper and early treatment of diabetes is cost effective since the implications of poor or late treatment are very expensive. In Finland, diabetes costs annually 505 million euros for the Finnish health care, and 90% of the care cost arises from treating the complications of diabetes [5]. These alarming facts promote the study of automatic diagnosis methods for screening over large 335 populations.…”

Suport visual details of X-ray image with plain information

“…However, the feature set must be crispy and precise, such that the time consumption can be reduced. The feature vector is  ISSN: 1693-6930 formed from the extracted features and saved for future reference [3]. During the testing phase, when the test image is passed, the features of the test image are extracted and are matched against train feature vector.…”