Linking Art through Human Poses We address the discovery of composition transfer in artworks based on their visual content. Automated analysis of large art collections, which are growing as a result of art digitization among museums and galleries, is an important tool for art history and assists cultural heritage preservation. Modern image retrieval systems offer good performance on visually similar artworks, but fail in the cases of more abstract composition transfer. The proposed approach links artworks through a pose similarity of human figures depicted in images. Human figures are the subject of a large fraction of visual art from middle ages to modernity and their distinctive poses were often a source of inspiration among artists. The method consists of two steps xe2x80x93 fast pose matching and robust spatial verification. We experimentally show that explicit human pose matching is superior to standard contentbased image retrieval methods on a manually annotated art composition transfer dataset.

Machine, Discourse and Power: From Machine Learning in Construction of 3D Face to Art and Creativity. The 3D face reconstruction based on a single image can be done recently by training the machine to learn from the database. The performance and accuracy of 3D facial structure and the geometry have largely improved with the powerful calculation of machine and its expanding data. The 3D face reconstruction can be applied in both still images and moving images and recently such technology draws attention in the art field such as portrait representation, facial recognition, surveillance art, etc. By using artificial intelligence (AI) technology and machine learning, artists are able to create artwork with hybridity characteristic, which consists of both human perspective, and machine creativity, which opens up a new gateway to the art world. The capability of artists in creating artwork, particularly portrait art in this research, has been enhanced in terms of technological and theoretical perspective. Artists can represent faces and understand 3D geometry in new ways by breaking through the traditional limitation; it also provides alternative methods in facial visualization beyond the expectation. Traditional expression and technique can be expended to a new dimension; the technology helps artists to think out of the box and continue to create new movement and ism throughout the art history. Moreover, the traditional belief about the importance of the originality of artistsxe2x80x99 mind, sense, perspective and techniques in creating artwork can now be redefined by machine and artificial intelligence. A new form in art and creativity, and even knowledge and history has been developed. This paper attempts to discuss the essential transformation in dimensional imagery and its aesthetic experiences through machine learning means, and the shift of discourse of power from human to machine.