6,504 research outputs found
Interactive retrieval of video using pre-computed shot-shot similarities
A probabilistic framework for content-based interactive video retrieval is described. The developed indexing of video fragments originates from the probability of the user's positive judgment about key-frames of video shots. Initial estimates of the probabilities are obtained from low-level feature representation. Only statistically significant estimates are picked out, the rest are replaced by an appropriate constant allowing efficient access at search time without loss of search quality and leading to improvement in most experiments. With time, these probability estimates are updated from the relevance judgment of users performing searches, resulting in further substantial increases in mean average precision
k-Nearest Neighbor Search for Large Scale High Dimensional data
Tohoku UniversityćŸłć±±
Multidimensional Range Queries on Modern Hardware
Range queries over multidimensional data are an important part of database
workloads in many applications. Their execution may be accelerated by using
multidimensional index structures (MDIS), such as kd-trees or R-trees. As for
most index structures, the usefulness of this approach depends on the
selectivity of the queries, and common wisdom told that a simple scan beats
MDIS for queries accessing more than 15%-20% of a dataset. However, this wisdom
is largely based on evaluations that are almost two decades old, performed on
data being held on disks, applying IO-optimized data structures, and using
single-core systems. The question is whether this rule of thumb still holds
when multidimensional range queries (MDRQ) are performed on modern
architectures with large main memories holding all data, multi-core CPUs and
data-parallel instruction sets. In this paper, we study the question whether
and how much modern hardware influences the performance ratio between index
structures and scans for MDRQ. To this end, we conservatively adapted three
popular MDIS, namely the R*-tree, the kd-tree, and the VA-file, to exploit
features of modern servers and compared their performance to different flavors
of parallel scans using multiple (synthetic and real-world) analytical
workloads over multiple (synthetic and real-world) datasets of varying size,
dimensionality, and skew. We find that all approaches benefit considerably from
using main memory and parallelization, yet to varying degrees. Our evaluation
indicates that, on current machines, scanning should be favored over parallel
versions of classical MDIS even for very selective queries
Design and Evaluation of a Probabilistic Music Projection Interface
We describe the design and evaluation of a probabilistic
interface for music exploration and casual playlist generation.
Predicted subjective features, such as mood and
genre, inferred from low-level audio features create a 34-
dimensional feature space. We use a nonlinear dimensionality
reduction algorithm to create 2D music maps of
tracks, and augment these with visualisations of probabilistic
mappings of selected features and their uncertainty.
We evaluated the system in a longitudinal trial in usersâ
homes over several weeks. Users said they had fun with the
interface and liked the casual nature of the playlist generation.
Users preferred to generate playlists from a local
neighbourhood of the map, rather than from a trajectory,
using neighbourhood selection more than three times more
often than path selection. Probabilistic highlighting of subjective
features led to more focused exploration in mouse
activity logs, and 6 of 8 users said they preferred the probabilistic
highlighting mode
Developing Interaction 3D Models for E-Learning Applications
Some issues concerning the development of interactive 3D models for e-learning applications are considered. Given that 3D data sets are normally large and interactive display demands high performance computation, a natural solution would be placing the computational burden on the client machine rather than on the server. Mozilla and Google opted for a combination of client-side languages, JavaScript and OpenGL, to handle 3D graphics in a web browser (Mozilla 3D and O3D respectively). Based on the O3D model, core web technologies are considered and an example of the full process involving the generation of a 3D model and their interactive visualization in a web browser is described. The challenging issue of creating realistic 3D models of objects in the real world is discussed and a method based on line projection for fast 3D reconstruction is presented. The generated model is then visualized in a web browser. The experiments demonstrate that visualization of 3D data in a web browser can provide quality user experience. Moreover, the development of web applications are facilitated by O3D JavaScript extension allowing web designers to focus on 3D contents generation
A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs
Cyber security is one of the most significant technical challenges in current
times. Detecting adversarial activities, prevention of theft of intellectual
properties and customer data is a high priority for corporations and government
agencies around the world. Cyber defenders need to analyze massive-scale,
high-resolution network flows to identify, categorize, and mitigate attacks
involving networks spanning institutional and national boundaries. Many of the
cyber attacks can be described as subgraph patterns, with prominent examples
being insider infiltrations (path queries), denial of service (parallel paths)
and malicious spreads (tree queries). This motivates us to explore subgraph
matching on streaming graphs in a continuous setting. The novelty of our work
lies in using the subgraph distributional statistics collected from the
streaming graph to determine the query processing strategy. We introduce a
"Lazy Search" algorithm where the search strategy is decided on a
vertex-to-vertex basis depending on the likelihood of a match in the vertex
neighborhood. We also propose a metric named "Relative Selectivity" that is
used to select between different query processing strategies. Our experiments
performed on real online news, network traffic stream and a synthetic social
network benchmark demonstrate 10-100x speedups over selectivity agnostic
approaches.Comment: in 18th International Conference on Extending Database Technology
(EDBT) (2015
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