Glen, FC, ND Smith and DP Crabb ( 2013).The curve clustering toolbox (cctoolbox). Probabilistic Curve-Aligned Clustering and Prediction with Regression Mixture Models, University of California, Irvine. Lecture Notes in Computer Science, 3522, 325–332. Object image retrieval by shape content in complex scenes using geometric constraints. Shape retrieval using triangle-area representation and dynamic space warping. Alajlan, N, I El Rube, MS Kamel and G Freeman ( 2007).The experimental results show that the extracted semantic notions of eyebrow shapes obtained by the proposed approach are much better than those by only utilizing 11 DTAR values or 12 DTAR values directly in terms of the consistency with human perceptions. To illustrate the effectiveness of the proposed approach, we use the AR and BJUT databases for experiments to demonstrate the consistency comparison with human perceptions.
Lastly, a similarity notion based on AFS is introduced via measuring the membership degrees of each eyebrow shape similar to the given reference shapes, and then one can describe each eyebrow shape by using two given reference eyebrow shapes via computing the membership degrees representing the relative similarities. Second, a descriptor of the landmarks is developed to represent selected reference eyebrows, and the corresponding DTAR curves are obtained for the selected reference eyebrows. First, 11 or 12 DTAR values are selected to describe eyebrows via considering the eyebrow corner information roughly, and then the corresponding DTAR curves are acquired via the cubic spline interpolation based on these selected points. In this paper, a revised directional triangle-area curve representation method (DTAR) is proposed to address the problem of eyebrow semantic shape characterization via curve representation.
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