We perform classification of ancient Roman Republican coins via recognizing their reverse motifs where various objects, faces, scenes, animals, and buildings are minted along with legends. Most of these coins are eroded due to their age and varying degrees of preservation, thereby affecting their informative attributes for visual recognition. Changes in the positions of principal symbols on the reverse motifs also cause huge variations among the coin types. Lastly, in-plane orientations, uneven illumination, and a moderate background clutter further make the task of classification non-trivial and challenging. To this end, we present a novel network model, CoinNet, that employs compact bilinear pooling, residual groups, and feature attention layers. Furthermore, we gathered the largest and most diverse image dataset of the Roman Republican coins that contains more than 18,000 images belonging to 228 different reverse motifs. On this dataset, our model achieves a classification accuracy of more than and outperforms the conventional bag-of-visual-words based approaches and more recent state-of-the-art deep learning methods. We also provide a detailed ablation study of our network and its generalization capability.
Unlike legends and portraits, the reverse motif is a discriminative visual cue that is less affected by wear and tear. Besides, a given reverse motif can be shared by coins of multiple classes. Therefore, the search space for the class of a given query coin image is aptly reduced by recognizing its reverse motif. This aspect makes the reverse motif-based coin classification coarse-grained that can further be refined by fine-grained classification methods @cite_17 .
@cite_17
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We propose a holistic system to classify ancient Roman Republican coins based on their reverse-side motifs. The bag-of-visual-words (BoW) model is enriched with spatial information to increase the discriminative power of the coin image representation. This is achieved by combining a spatial pooling scheme with co-occurrence encoding of visual words. We specifically address the required geometric invariance properties of image-based ancient coin classification, as coins from different collections can be located at differing image locations, have various scales in the images and can undergo various in-plane rotations. We evaluate our method on a data set of 2,224 coin images from three different sources. The experimental results show that our proposed image representation is more discriminative than the traditional bag-of-visual-words model while still being invariant to the mentioned geometric transformations. For 29 motifs, the system achieves a classification rate of 82 . It is considered to act as a helpful tool for numismatists in the near future, which facilitates and supports the traditional coin classification process by a faster presorting of coins. Classification of ancient coins is a substantial part of numismatic research which needs a large amount of expert knowledge due to the high number of classes to be considered. In this paper we propose an automatic image-based classification method for ancient coins to support this time-consuming and difficult process. We demonstrate that previously proposed learning-based methods suffer from the practical conditions of this problem: a high number of classes, limited number of training samples per class and complex intra-class variations. As a solution we propose a similarity metric based on feature correspondence which is designed to be robust against the possible intra-class coin variations like degraded parts, non-rigid deformations and illumination-induced appearance changes. The similarity metric is used in an exemplar-based ancient coin classification scheme which shows to outperform previously proposed methods for ancient coin recognition. Experiments are conducted on a dataset of 60 Roman Republican coin classes where the presented method achieves classification rates ranging from 72.7 for the case of one training sample per class up to 97.2 when nine training samples per class are used. Time series motifs are approximately repeating patterns in real-valued time series data. They are useful for exploratory data mining and are often used as inputs for various time series clustering, classification, segmentation, rule discovery, and visualization algorithms. Since the introduction of the first motif discovery algorithm for univariate time series in 2002, multiple efforts have been made to generalize motifs to the multidimensional case. In this work, we show that these efforts, which typically attempt to find motifs on all dimensions, will not produce meaningful motifs except in the most contrived situations. We explain this finding and introduce mSTAMP, an algorithm that allows meaningful discovery of multidimensional motifs. Beyond producing objectively and subjectively meaningful results, our algorithm has a host of additional advantages, including being much faster, requiring fewer parameters and supporting streaming data. We demonstrate the utility of our mSTAMP-based motif discovery framework on domains as diverse as audio processing, industry, and sports analytics. Time series motifs are repeated patterns in long and noisy time series. Motifs are typically used to understand the dynamics of the source because repeated patterns with high similarity evidentially rule out the presence of noise. Recently, time series motifs have also been used for clustering, summarization, rule discovery and compression as features. For all such purposes, many high quality motifs of various lengths are desirable and thus, originates the problem of enumerating motifs for a wide range of lengths. Existing algorithms find motifs for a given length. A trivial way to enumerate motifs is to run one of the algorithms for the whole range of lengths. However, such parameter sweep is computationally infeasible for large real datasets. In this paper, we describe an exact algorithm, called MOEN, to enumerate motifs. The algorithm is an order of magnitude faster than the naive algorithm. The algorithm frees us from re-discovering the same motif at different lengths and tuning multiple data-dependent parameters. The speedup comes from using a novel bound on the similarity function across lengths and the algorithm uses only linear space unlike other motif discovery algorithms. We describe three case studies in entomology and activity recognition where MOEN enumerates several high quality motifs.