Showing papers by "Kai Li published in 2009"

ImageNet: A large-scale hierarchical image database

Abstract: The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.

Directing experimental biology: a case study in mitochondrial biogenesis.

Abstract: Computational approaches have promised to organize collections of functional genomics data into testable predictions of gene and protein involvement in biological processes and pathways. However, few such predictions have been experimentally validated on a large scale, leaving many bioinformatic methods unproven and underutilized in the biology community. Further, it remains unclear what biological concerns should be taken into account when using computational methods to drive real-world experimental efforts. To investigate these concerns and to establish the utility of computational predictions of gene function, we experimentally tested hundreds of predictions generated from an ensemble of three complementary methods for the process of mitochondrial organization and biogenesis in Saccharomyces cerevisiae. The biological data with respect to the mitochondria are presented in a companion manuscript published in PLoS Genetics (doi:10.1371/journal.pgen.1000407). Here we analyze and explore the results of this study that are broadly applicable for computationalists applying gene function prediction techniques, including a new experimental comparison with 48 genes representing the genomic background. Our study leads to several conclusions that are important to consider when driving laboratory investigations using computational prediction approaches. While most genes in yeast are already known to participate in at least one biological process, we confirm that genes with known functions can still be strong candidates for annotation of additional gene functions. We find that different analysis techniques and different underlying data can both greatly affect the types of functional predictions produced by computational methods. This diversity allows an ensemble of techniques to substantially broaden the biological scope and breadth of predictions. We also find that performing prediction and validation steps iteratively allows us to more completely characterize a biological area of interest. While this study focused on a specific functional area in yeast, many of these observations may be useful in the contexts of other processes and organisms.

Parallel rendering with K-way replication

Abstract: With the recent advances in commodity graphics hardware performance, PC clusters have become an attractive alternative to traditional high-end graphics workstations. The main challenge is to develop parallel rendering algorithms that work well within the memory constraints and communication limitations of a networked cluster. Previous systems have required the entire 3D scene to be replicated in memory on every PC. While this approach can take advantage of view-dependent load balancing algorithms and thus largely avoid the problems of inter-process communication, it limits the scalability of the system to the memory capacity of a single PC. We present a k-way replication approach in which each 3D primitive of a large scene is replicated on k out of n PCs (k/spl Lt/n). The key idea is to support 3D models larger than the memory capacity of any single PC, while retaining the reduced communication overheads of dynamic view-dependent partitioning. In this paper, we investigate algorithms for distributing copies of primitives among PCs and for dynamic load balancing under the constraints of partial replication. Our main result is that the parallel rendering efficiencies achieved with small replication factors are similar to the ones measured with full replication. By storing one-fourth of Michelangelo's David model (800 MB) on each of 24 PCs (each with 256 MB of memory), our system is able to render 40 million polygons/second (65 % efficiency).

Tech-note: Device-free interaction spaces

Abstract: Existing approaches to 3D input on wall-sized displays include tracking users with markers, using stereo- or depth-cameras or have users carry devices like the Nintendo Wiimote. Markers makes ad hoc usage difficult, and in public settings devices may easily get lost or stolen. Further, most camera-based approaches limit the area where users can interact.