When querying a huge image database containing millions of images, the result of the query may still contain many thousands of images that need to be presented to the user. We consider the problem of arranging such a large set of images into a visually coherent layout, one that places similar images next to each other. Image similarity is determined using a bag-of-features model, and the layout is constructed from a hierarchical clustering of the image set by mapping an in-order traversal of the hierarchy tree into a space-filling curve. This layout method provides strong locality guarantees so we are able to quantitatively evaluate performance using standard image retrieval benchmarks. Performance of the bag-of-features method is best when the vocabulary is learned on the image set being clustered. Because learning a large, discriminative vocabulary is a computationally demanding task, we present a novel method for efficiently adapting a generic visual vocabulary to a particular dataset. We evaluate our clustering and vocabulary adaptation methods on a variety of image datasets and show that adapting a generic vocabulary to a particular set of images improves performance on both hierarchical clustering and image retrieval tasks.
Analysts who work with collections of multimedia to perform information foraging understand how difficult it is to
connect information across diverse sets of mixed media. The wealth of information from blogs, social media, and news
sites often can provide actionable intelligence; however, many of the tools used on these sources of content are not
capable of multimedia analysis because they only analyze a single media type. As such, analysts are taxed to keep a
mental model of the relationships among each of the media types when generating the broader content picture. To
address this need, we have developed Canopy, a novel visual analytic tool for analyzing multimedia. Canopy provides
insight into the multimedia data relationships by exploiting the linkages found in text, images, and video co-occurring in
the same document and across the collection. Canopy connects derived and explicit linkages and relationships through
multiple connected visualizations to aid analysts in quickly summarizing, searching, and browsing collected information
to explore relationships and align content. In this paper, we will discuss the features and capabilities of the Canopy
system and walk through a scenario illustrating how this system might be used in an operational environment.
High throughput instrumentation for genomics is producing data orders of magnitude greater than even a decade before. Biologists often visualize the data of these experiments through the use of heat maps. For large datasets, heat map visualizations do not scale. These visualizations are only capable of displaying a portion of the data, making it difficult for scientists to find and detect patterns that span more than a subsection of the data. We present a novel method that provides an interactive visual display for massive heat maps
[O(108)]. Our process shows how a massive heat map can be decomposed into multiple levels of abstraction to represent the underlying macrostructures. We aggregate these abstractions into a framework that can allow near real-time navigation of the space. To further assist pattern discovery, we ground our system on the principle of focus+context. Our framework also addresses the issue of balancing the memory and display resolution and heat map size. We will show that this technique for biologists provides a powerful new visual metaphor for analyzing massive datasets.
The United States Department of Energy is facing a large task in characterizing and remediating waste tanks and their contents. Because of the hazardous materials inside the waste tanks, all of the work must be done remotely. The purpose of this paper is to show how to reconstruct an enclosed environment from various scans of a Laser Range Finder. The reconstructed environment can then be used by a robot for path planning, and by an operator to monitor the progress of the waste remediation process. Environment reconstruction consists of two tasks: image processing and laser sculpting. The image processing task focuses first on reducing the quantity of low-confidence data and on smoothing random fluctuations in the data. Then the processed range data must be converted into an XYZ Cartesian coordinate space, a process for which we examined two methods. The first method is a geometrical transform of the LRF data. The second uses an artificial neural network to transform the data to XYZ coordinates. Once an XYZ data set is computed, laser sculpting can be performed. Laser sculpting employs a hierarchical tree structure formally called an octree. The octree structure allows efficient storage of volumetric data and the ability to fuse multiple data sets. Our research has allowed us to examine the difficulties of fusing multiple LRF scans into an octree and to develop algorithms for converting an octree structure into a representation of polygon surfaces.
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