Content-Based Image Retrieval

Abstract

Content-Based Image Retrieval (CBIR) has a useful role in image retrieval framework. It is a commonly received solution for an efficient and effective method that can look up for the required image from the large database without human interaction. There occurs a need of CBIR because the development digital images, due to widespread capturing of images using web cameras, mobile phones enable with the camera, and digital cameras is rendering the management of image database tedious. It can also be used in other application such as web engines and social media which stores a large number of images and requires fast retrieval of the image selected by the user.

The extraction of a feature in CBIR is a noticeable step whose viability is dependent upon the strategy used for feature extraction from given images. These features can be arranged in classes like as histogram, spatial layout, shape, texture, color, etc.The CBIR uses these features to retrieve and index the image database. The objective is to find a unique representation for all different variation because the user captures images in various conditions such as occlusion and varying illumination etc. Feature extraction method used for CBIR are Local tetra patterns (LTrP) and Dual-Cross Patterns (DCP). Local tetra patterns (LTrP) is a method which acquires more detailed information by using four possible
directions of every center pixel in an image, and is calculated from first order derivatives in horizontal and vertical directions. DCP encodes second order information in the vertical, horizontal and diagonal direction, by performing the encoding of sample points in the local
surrounding region of every center pixel in an image.

Simulation is performed in Matlab 8.6 to evaluate the performance of retrieval framework using LTrP and DCP, and Corel 900 database(D) is used for this purpose. The simulation performance of presented technique is evaluated in terms of average recall and average precision. The average recall for LTrP is 42% and the average precision for LTrP is 63%. The average recall for DCP is 35% and the average precision for DCP is 61.05%. Our extensive simulation on Corel database shows that the DCP technique has better computational complexity compared to LTrP methods.