Comparative Study of CBIR Systems Based on PCA and SIFTS Feature Extraction Methods

Abstract

Content-aware image retrieval is a very important topic nowadays, when the amount of digital image data is highly increasing. Existing PCA (principle component analysis) based image retrieval systems perform at a reduced level on real life images, where background data may distort image descriptors and retrieval results. To avoid this, a preprocessing step is introduced in this paper to distinguish between foreground and background, using integrated saliency detection. To build the descriptor only on the most relevant pixels, orientation feature is extracted at salient Modified Harris for Edges and Corners (MHEC) key points using an improved edge map, resulting in a Salient Orientation Histogram (SOH). The proposed CBIR system is also augmented with a segmentation step for object detection. The method is tested on the CORAL database, containing random internet images. Image retrieval and object detection both give promising results compared to other state-of-the-art methods. In this study, PCA based image retrieval system and proposed CBIR are compared for performance analysis. The proposed method yields good performance results.