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This article studies two types of shape representations in a bag-of-words-based pipeline to recognize Maya glyphs. The first is a knowledge-driven Histogram of Orientation Shape Context (HOOSC) representation, and the second is a data-driven representation obtained by applying an unsupervised Sparse Autoencoder (SA). In addition to the glyph data, the generalization ability of the descriptors is investigated on a larger-scale sketch dataset. The contributions of this article are four-fold: (1) the evaluation of the performance of a data-driven auto-encoder approach for shape representation; (2) a comparative study of hand-designed HOOSC and data-driven SA; (3) an experimental protocol to assess the effect of the different parameters of both representations; and (4) bridging humanities and computer vision\/machine learning for Maya studies, specifically for visual analysis of glyphs. From our experiments, the data-driven representation performs overall in par with the hand-designed representation for similar locality sizes on which the descriptor is computed. We also observe that a larger number of hidden units, the use of average pooling, and a larger training data size in the SA representation all improved the descriptor performance. Additionally, the characteristics of the data and stroke size play an important role in the learned representation.<\/jats:p>","DOI":"10.1145\/2905369","type":"journal-article","created":{"date-parts":[[2016,9,20]],"date-time":"2016-09-20T14:18:28Z","timestamp":1474381108000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Evaluating Shape Representations for Maya Glyph Classification"],"prefix":"10.1145","volume":"9","author":[{"given":"G\u00fclcan","family":"Can","sequence":"first","affiliation":[{"name":"Idiap Research Institute and \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Martigny, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Marc","family":"Odobez","sequence":"additional","affiliation":[{"name":"Idiap Research Institute and \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Martigny, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Gatica-Perez","sequence":"additional","affiliation":[{"name":"Idiap Research Institute and \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Martigny, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,9,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1155\/2010\/919367"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-015-2523-7"},{"key":"e_1_2_1_3_1","volume-title":"Conference on Advances in Neural Information Processing Systems","volume":"2","author":"Belongie Serge","year":"2000","unstructured":"Serge Belongie , Jitendra Malik , and Jan Puzicha . 2000 . Shape context: A new descriptor for shape matching and object recognition . In Conference on Advances in Neural Information Processing Systems , Vol. 2 . 3. Serge Belongie, Jitendra Malik, and Jan Puzicha. 2000. Shape context: A new descriptor for shape matching and object recognition. In Conference on Advances in Neural Information Processing Systems, Vol. 2. 3."},{"volume-title":"Conference on Advances in Neural Information Processing Systems 22","author":"Bengio Yoshua","key":"e_1_2_1_4_1","unstructured":"Yoshua Bengio and James S. Bergstra . 2009. Slow, decorrelated features for pretraining complex cell-like networks . In Conference on Advances in Neural Information Processing Systems 22 . 99--107. Yoshua Bengio and James S. Bergstra. 2009. Slow, decorrelated features for pretraining complex cell-like networks. 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