5,156 research outputs found
Asymptotically near-optimal RRT for fast, high-quality, motion planning
We present Lower Bound Tree-RRT (LBT-RRT), a single-query sampling-based
algorithm that is asymptotically near-optimal. Namely, the solution extracted
from LBT-RRT converges to a solution that is within an approximation factor of
1+epsilon of the optimal solution. Our algorithm allows for a continuous
interpolation between the fast RRT algorithm and the asymptotically optimal
RRT* and RRG algorithms. When the approximation factor is 1 (i.e., no
approximation is allowed), LBT-RRT behaves like RRG. When the approximation
factor is unbounded, LBT-RRT behaves like RRT. In between, LBT-RRT is shown to
produce paths that have higher quality than RRT would produce and run faster
than RRT* would run. This is done by maintaining a tree which is a sub-graph of
the RRG roadmap and a second, auxiliary graph, which we call the lower-bound
graph. The combination of the two roadmaps, which is faster to maintain than
the roadmap maintained by RRT*, efficiently guarantees asymptotic
near-optimality. We suggest to use LBT-RRT for high-quality, anytime motion
planning. We demonstrate the performance of the algorithm for scenarios ranging
from 3 to 12 degrees of freedom and show that even for small approximation
factors, the algorithm produces high-quality solutions (comparable to RRG and
RRT*) with little running-time overhead when compared to RRT
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can
be seen in an image to bridge the semantic gap, this survey considers what
people tag about an image. A comprehensive treatise of three closely linked
problems, i.e., image tag assignment, refinement, and tag-based image retrieval
is presented. While existing works vary in terms of their targeted tasks and
methodology, they rely on the key functionality of tag relevance, i.e.
estimating the relevance of a specific tag with respect to the visual content
of a given image and its social context. By analyzing what information a
specific method exploits to construct its tag relevance function and how such
information is exploited, this paper introduces a taxonomy to structure the
growing literature, understand the ingredients of the main works, clarify their
connections and difference, and recognize their merits and limitations. For a
head-to-head comparison between the state-of-the-art, a new experimental
protocol is presented, with training sets containing 10k, 100k and 1m images
and an evaluation on three test sets, contributed by various research groups.
Eleven representative works are implemented and evaluated. Putting all this
together, the survey aims to provide an overview of the past and foster
progress for the near future.Comment: to appear in ACM Computing Survey
Fuzzy Integration to Standard Calculation of K-Nearest Neighbour Attributes
The development of information and data in the era of the industrial revolution 4.0 is very fast. Researchers, institutions and even industry are competing to find and utilize methods in data processing that are more effective and efficient. In data mining classification, there are several best methods and are widely used by researchers. One of them is K-Nearest Neighbor (KNN). The calculation process in the KNN algorithm is carried out by comparing the testing data to all existing training data. This comparison is generally symbolized by the value of closeness or similarity between attribute records. The KNN method is proven to be good for handling large datasets and datasets with many attributes. One of the drawbacks in calculating the similarity of the KNN is that if there are attributes with a large range value, the similarity value will also be large. Conversely, if the range in an attribute is small, the similarity is also small. This condition is clearly unfair considering the types of attributes in the current data vary widely. One solution to this problem is to use standardization for all existing data attributes. Fuzzy is a model introduced by Prof. Zadeh which allows a faint value to be a value between 1 and 0. In this study the fuzzy model will be integrated in the KNN similarity calculation to obtain standardization of all data attributes. The results show that the use of the KNN algorithm in the classification of credit approval has an accuracy rate of 91.83%
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