Comparative analysis of Middle Stone Age artifacts in Africa (CoMSAfrica)

Spatial and temporal variation among African Middle Stone Age (MSA) archeological assemblages provide essential cultural and behavioral data for understanding the origin, evolution, diversification, and dispersal of Homo sapiens—and, possibly, interactions with other hominin taxa. However, incorporating archeological data into a robust framework suited to replicable, quantitative analyses that can be integrated with observations drawn from studies of the human genome, hominin morphology, and paleoenvironmental contexts requires the development of a unified comparative approach and shared units of analysis. Lithic (stone) artifacts provide the fundamental source of information for continental‐scale comparisons of past hominin behavior because they quantitatively dominate the Paleolithic record, and unlike organic artifacts made of bone or shell, they are preserved in a larger variety of depositional settings. However, attempts to integrate African MSA lithic data from different periods or regions have suffered from divergent research traditions among archeologists that employ incompatible approaches, definitions, and data collection methods. Communication among analysts is further constrained by the presence of varied theoretical and methodological schools, including analytical grammars that may represent distinct ways of viewing, describing, measuring, and interpreting the world (i.e., attribute analysis vs. chaîne opératoire). These issues are further exacerbated by differences in geography, geology, ecology, and research intensity between different parts of Africa. Archeologists across Africa thus lack a common, intersubjective and transparent system for lithic analysis, with currently few shared basic definitions or protocols of measurements. Yet, objectivity and replicability are two functional requirements of science

Finding Near-Duplicate Videos in Large-Scale Collections

This chapter discusses the problem of Near-Duplicate Video Retrieval (NDVR). The main objective of a typical NDVR approach is: given a query video, retrieve all near-duplicate videos in a video repository and rank them based on their similarity to the query. Several approaches have been introduced in the literature, which can be roughly classified in three categories based on the level of video matching, i.e., (i) video-level, (ii) frame-level, and (iii) filter-and-refine matching. Two methods based on video-level matching are presented in this chapter. The first is an unsupervised scheme that relies on a modified Bag-of-Words (BoW) video representation. The second is a s upervised method based on Deep Metric Learning (DML). For the development of both methods, features are extracted from the intermediate layers of Convolutional Neural Networks and leveraged as frame descriptors, since they offer a compact and informative image representation, and lead to increased system efficiency. Extensive evaluation has been conducted on publicly available benchmark datasets, and the presented methods are compared with state-of-the-art approaches, achieving the best results in all evaluation setups