Search

The 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 ofRGBquery images toL*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. [ABSTRACT FROM AUTHOR]

The paper investigates a novel visual concept-based biomedical image querying mechanism to manage with a wide range of medical images acquired under the similar class/modality. Initially, the approach converts RGB images toL*a*b* dimensions.L*a*b* triplets are then partitioned into sub-regions and subsequently modelled by an unskewed Gaussian function for image characterisation based on standardised moments, that represents visual features. The features are then blended to formulate visual concepts and later vector quantised by geodesic self-organising-map to build a codebook of concepts. A global relationship among the clustered concepts is further established through dual correlation matrices and used in a distance metric for matching the given query with target images. Additionally, an enhanced inverted indexing mechanism is introduced to reduce the search time of this query expansion framework. Systematic evaluation of retrieval results in a heterogeneous medical image collection of 30 000 images of different modalities, body parts and orientations, demonstrates 50% reduction in computation time and 20–30% increase in precision at each recall level in comparison with individual visual features. Further experimental analysis employing effectiveness, robustness and precision at 20 parameters validates the benefits of the proposed scheme. [ABSTRACT FROM AUTHOR]