Call for Papers

In the last decades, the extensive research on recognition/classification has mainly focused on distinguishing object classes, which are somehow dissimilar. Recently, the computer vision, machine learning and multimedia scientific community has addressed with increasing interest the problem of fine-grained recognition: it refers to a subordinate level of recognition, such as recognizing different species of animals (e.g., dogs, birds, plants) in different types of multimedia (e.g., audio, images, videos). Of course, this task represents a harder challenge than the “basic” object recognition, because the discriminative features among the object classes are more subtle and difficult to identify. Automated systems performing such tasks might provide significant support to many applications, especially those requiring specialized domain knowledge (e.g. ecology): indeed, most people can easily distinguish between a person playing a clarinet from one holding a clarinet, while it is much more difficult to distinguish between plant types or animal species, where inter-class similarity might be very high. Moreover, especially for the ecological context, the need for such automatic tools has become even greater due to technological advances leading to a massive collection of multimedia content (images, videos and audios) whose analysis still requires the employment of expert human operators.