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A Feature clustering approach based on histogram of oriented optical flow and superpixels

Show simple item record Bandara, AMRR Ranathunga, L Abdullah, NA 2019-07-15T10:01:22Z 2019-07-15T10:01:22Z
dc.description.abstract Visual feature clustering is one of the cost-effective approaches to segment objects in videos. However, the assumptions made for developing the existing algorithms prevent them from being used in situations like segmenting an unknown number of static and moving objects under heavy camera movements. This paper addresses the problem by introducing a clustering approach based on superpixels and short-term Histogram of Oriented Optical Flow (HOOF). Salient Dither Pattern Feature (SDPF) is used as the visual feature to track the flow and Simple Linear Iterative Clustering (SLlC) is used for obtaining the superpixels. This new clustering approach is based on merging superpixels by comparing short term local HOOF and a color cue to form high-level semantic segments. The new approach was compared with one of the latest feature clustering approaches based on K-Means in eight-dimensional space and the results revealed that the new approach is better by means of consistency, completeness, and spatial accuracy. Further, the new approach completely solved the problem of not knowing the number of objects in a scene. en_US
dc.language.iso en en_US
dc.subject SDPF; HOOF; Superpixe/; Clustering; Object Segmentation; ego-motion en_US
dc.title A Feature clustering approach based on histogram of oriented optical flow and superpixels en_US
dc.type Conference-Abstract en_US
dc.identifier.faculty IT en_US
dc.identifier.department Department of Information Technology en_US
dc.identifier.year 2015 en_US
dc.identifier.conference IEEE 10th International Conference on Industrial and Information Systems - (ICIIS) 2015 en_US
dc.identifier.pgnos pp. 480 - 484 en_US en_US en_US

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