@article{MAKHILLAJIT20171666668,
    title = {A New View Based Algorithm for Non-Rigid 3D Object Retrieval},
    journal = {Asian Journal of Information Technology},
    volume = {16},
    number = {6},
    pages = {576-587},
    year = {2017},
    issn = {1682-3915},
    doi = {ajit.2017.576.587},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2017.576.587},
    author = {H.,H.,S. and},
    keywords = {Content based retrieval,non-rigid 3D objects retrieval,SIFT,ERC trees,extracted,easier to discriminate},
    abstract = {This study introduces an efficient view based algorithm for 3D non-rigid object retrieval that accepts
all the models. The algorithm is based on describing the 3D Model using multi-scale local visual features. To
reduce the cost of distance computation and features storage, the extracted features integrated into histogram
using Bag of Features (BoF) approach. The proposed algorithm used only 42 depth images to extract the
features which reduced the runtime. The proposed Enhanced Ray Tracing algorithm was used for generating
best quality depth images with great reduction in runtime also. The codebook for the vector quantization is
learned via. our proposed Modifed Extremely Randomized Clustering trees (MERC-trees). In comparison
with other algorithms, the proposed algorithm achieved high performance on SHREC’11 and 15 datasets; the
well-known benchmarks. Experimental results show that, the proposed algorithm is very fast, achieved high
retrieval result and robust against 11 types of different transformations with different levels of strength when
tested on SHREC’11-Robust dataset. The proposed algorithm achieved the 6th best performance among
37 methods participated in SHREC’15 contest which none of them was view based and all used topological
based features that are easier to discriminate the non-rigid objects.}
    }