Abstract
Similarity computation between bio images is very different from the similarity computation between routine images as the information about bio images are not completely in the form of textual nature. Like in case of bio-images (viz. protein structures) similarity can be computed on the basis of their sequence similarity and their structural similarity. In the previous work AMIPRO, has been proposed which takes both structural and sequential similarity into the consideration. Intelligent Vision Algorithm has been applied on the protein images and the sequence information present in the PDB file has been used to determine the sequential similarity. The proposed model minimizes the time taken in the online similarity determination by storing the pre calculated ranked results. In present manuscript a storage efficient index structure Multi Minimum Product Spanning Tree has been proposed and applied on AMIPRO. Our results shows that MMPST based implementation of index structure can easily map similar records on the basis of their level of similarity computed on multiple features.
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Srivastava, M., Singh, S.K., Abbas, S.Q. (2018). Multi Minimum Product Spanning Tree Based Indexing Approach for Content Based Retrieval of Bio Images. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-10-8660-1_73
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