Indexing Mayan hieroglyphs with neural codes
Edgar Román-Rangel, Stéphane Marchand‐Maillet · 2016
We present an approach for unsupervised computation of local shape descriptors, which relies on the use of linear autoencoders for characterizing local regions of complex shapes. The proposed approach responds to the need for a robust scheme to index binary images using local descriptors, which arises when only few examples of the complete images are available for training, thus making inaccurate the learning process of parameters of traditional neural networks schemes. Given the possibility of using linear operations during the encoding phase, the computation of the proposed local descriptor can be fast once the parameters of the encoding function are learned. After conducting a vast search, we identified the optimal dimensionality of the resulting local descriptor to be of only 128 dimensions, which allows for efficient further operations on them, such as the construction of bag representations with purposes of shape retrieval and classification. We evaluated the proposed approach indexing a collection of complex binary images, whose instances contain compounds of hieroglyphs from the ancient Maya civilization. Our retrieval experiments show that the proposed approach achieves competitive retrieval performance when compared with hand-crafted local descriptors.