An Efficient Image Retrieval Method Using Fused Heterogeneous Feature

Abstract

Images exhibit significant variabilities that make each image different from others, even if though they belong to the same class or categories. The lack of affiliation between the heterogeneous features and the structure of images makes it challenging to model these variations for automatic image recognition and retrieval. Recognizing similar images is still a challenging task when a highly variable textured and heterogeneous features image is given as a query. In this paper, heterogeneous local penta is proposed that precisely extracts heterogeneous patterns from an image. The heterogeneity index computed over neighborhood pixels is incorporated with local penta pattern, so that it can retrieve appropriate images by extracting heterogeneous features. In this paper, a local penta pattern fused with heterogeneous features is proposed that precisely extracts heterogeneous patterns of images. The heterogeneity index computed over neighborhood pixels is incorporated with local penta pattern, so that it can retrieve appropriate images by extracting heterogeneous features. The proposed method is applied over six datasets, namely Corel 1000, Brodatz, STex, Indian movie face database, FigureQA, and our hand-crafted chart dataset. These datasets are having a highly variable and heterogeneous texture, characteristics, and properties. Experimental results depict that heterogeneous feature extracted could retrieve and recognize images with a high accuracy rate. Experimental results depict that the proposed method increases the retrieval rate by 10–15% in terms of average precision and average recall, as compared with the customary methods.