  In this paper, we present a decision level fused local Morphological Pattern
Spectrum(PS) and Local Binary Pattern (LBP) approach for an efficient shape
representation and classification. This method makes use of Earth Movers
Distance(EMD) as the measure in feature matching and shape retrieval process.
The proposed approach has three major phases : Feature Extraction, Construction
of hybrid spectrum knowledge base and Classification. In the first phase,
feature extraction of the shape is done using pattern spectrum and local binary
pattern method. In the second phase, the histograms of both pattern spectrum
and local binary pattern are fused and stored in the knowledge base. In the
third phase, the comparison and matching of the features, which are represented
in the form of histograms, is done using Earth Movers Distance(EMD) as metric.
The top-n shapes are retrieved for each query shape. The accuracy is tested by
means of standard Bulls eye score method. The experiments are conducted on
publicly available shape datasets like Kimia-99, Kimia-216 and MPEG-7. The
comparative study is also provided with the well known approaches to exhibit
the retrieval accuracy of the proposed approach.
