Precise shape matching of large shape datasets using hybrid approach

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dc.contributor.author Shehzad Khalid
dc.contributor.author Bushra Sabir
dc.contributor.author Sohail Jabbar
dc.contributor.author Naveen Chilamkurti
dc.date.accessioned 2018-08-09T06:06:59Z
dc.date.available 2018-08-09T06:06:59Z
dc.date.issued 2017
dc.identifier.uri http://hdl.handle.net/123456789/7196
dc.description.abstract Precise and fast shape matching and retrieval from very large datasets is a challenging task because of the existence of many distortions such as noise, occlusion and affine distortions. In this paper, we aim to propose a time-saving and effective shape matching and retrieval framework, that employs pruning which will enable online shape retrieval from extremely large datasets. First, using a hierarchical treebased structure supporting parallel processing and efficient feature descriptors, irrelevant shapes are pruned and a subset of shapes relevant to the query is selected, then using more sophisticated feature descriptors, accurate retrieval is performed. Contour representation of an object is considered as most significant visual shape similarity measure by the humans. Using boundary information, we generate two simplified and efficient feature descriptors for fast pruning, and a sophisticated feature descriptor for effective and accurate retrieval. Tests performed on standard datasets unveil that the proposed technique is computationally more efficient than the state-of-the-art techniques while maintaining comparable matching and retrieval performance. Its performance is scalable for huge datasets and is robust against affine transformations, articulations and occlusion. en_US
dc.language.iso en en_US
dc.publisher Bahria University Islamabad Campus en_US
dc.subject Computer Engineering en_US
dc.title Precise shape matching of large shape datasets using hybrid approach en_US
dc.type Article en_US


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