ABSTRACT
Searching a large document collection to learn about a broad subject involves the iterative process of figuring out what to ask, filtering the results, identifying useful documents, and deciding when one has covered enough material to stop searching. We are calling this activity "discoverage," discovery of relevant material and tracking coverage of that material. We built a visual analytic tool called Footprints that uses multiple coordinated visualizations to help users navigate through the discoverage process. To support discovery, Footprints displays topics extracted from documents that provide an overview of the search space and are used to construct searches visuospatially. Footprints allows users to triage their search results by assigning a status to each document (To Read, Read, Useful), and those status markings are shown on interactive histograms depicting the user's coverage through the documents across dates, sources, and topics. Coverage histograms help users notice biases in their search and fill any gaps in their analytic process. To create Footprints, we used a highly iterative, user-centered approach in which we conducted many evaluations during both the design and implementation stages and continually modified the design in response to feedback.
ABSTRACT
In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties. Many innovative and useful methods currently exist for creating novel objects and object categories (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings. First, shape variations are generally imposed by the experimenter, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints. Second, the existing methods have difficulty capturing the shape complexity of natural objects. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases. Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms. Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection. Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics. The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of real-world objects. Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background. The virtual objects can also be 'printed' as haptic objects using a conventional 3-D prototyper. We also describe some implementations of these computational algorithms to help illustrate the potential utility of the algorithms. It is important to distinguish the algorithms from their implementations. The implementations are demonstrations offered solely as a 'proof of principle' of the underlying algorithms. It is important to note that, in general, an implementation of a computational algorithm often has limitations that the algorithm itself does not have. Together, these methods represent a set of powerful and flexible tools for studying object recognition and perceptual learning by biological and computational systems alike. With appropriate extensions, these methods may also prove useful in the study of morphogenesis and phylogenesis.
