Fuzzy-linked phase congruency-based feature descriptors for image retrieval

Abstract

ABSTRACTThe demand for acute and compact feature descriptors remains a key issue of concern for prevailing image retrieval schemes. This paper offers dual feature descriptors characterised by phase congruency (PC) and fuzzy logic for image indexing and retrieval. The proposed mechanism commences with colour space conversion of RGB query images to L*a*b* triplets and further application of PC generates relevant feature information. The ensuing visual features are blended by fuzzy rules to formulate the unified feature histograms and later fuzzy quantised to produce two feature descriptors termed as PC-based colour edge directivity descriptor (PC-CEDD), PC-based fuzzy colour texture histogram (PC-FCTH). The resulting descriptors occupy minimal storage space of 23–74 bytes per image, with 60% reduction in feature extraction time in comparison with CEDD, FCTH. Relative precision–recall and mean average precision (MAP) analysis of the intended feature histograms on medical, texture, and object picture dataset signify the improvement in retrieval performance. Furthermore, average normalised modified retrieval rank analysis of the intended descriptors reveals the better matching quality of the given query image.