Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers
نویسندگان
چکیده مقاله:
This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, MATLAB Message Passing Interface (MPI) functions and features included in the toolbox help us obtain good performance by two schemes of task-parallelization and dataparallelization models. Parallel SPK algorithm ran over a cluster of computers and achieved less run time. A speedup value equal to 13 is obtained for a configuration with up to 5 Quad processors.
منابع مشابه
Dimension Amnesic Pyramid Match Kernel
With the success of local features in object recognition, feature-set representations are widely used in computer vision and related domains. Pyramid match kernel (PMK) is an efficient approach to quantifying the similarity between two unordered feature-sets, which allows well established kernel machines to learn with such representations. However, the approximation of PMK to the optimal featur...
متن کاملRobust object tracking using a spatial pyramid heat kernel structural information representation
In this paper, we propose an object tracking framework based on a spatial pyramid heat kernel structural information representation. In the tracking framework, we take advantage of heat kernel structural information (HKSI) matrices to represent object appearance, because HKSI matrices perform well in characterizing the edge flow (or structural) information on the object appearance graph. To fur...
متن کاملObject Recognition based on Local Steering Kernel and SVM
The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...
متن کاملThe Pyramid Match Kernel: Efficient Learning with Sets of Features
In numerous domains it is useful to represent a single example by the set of the local features or parts that comprise it. However, this representation poses a challenge to many conventional machine learning techniques, since sets may vary in cardinality and elements lack a meaningful ordering. Kernel methods can learn complex functions, but a kernel over unordered set inputs must somehow solve...
متن کاملObject Recognition Using Prediction And Probabilistic Match
PREMIO is a CAD-based object recognition and localization system that uses CAD models of 3D objects and knowledge of lighting and sensors to predict the detectability of features in various views of the object. The predictions that PREMIO produces are powerful new tools in recognizing and determining the pose of a 3D object. In order to take advantage of these tools, we have developed a new mat...
متن کاملRecognizing in the depth: Selective 3D Spatial Pyramid Matching Kernel for object and scene categorization
This paper proposes a novel approach to recognize object and scene categories in depth images. We introduce a Bag of Words (BoW) representation in 3D, the Selective 3D Spatial Pyramid Matching Kernel (3DSPMK). It starts quantizing 3D local descriptors, computed from point clouds, to build a vocabulary of 3D visual words. This codebook is used to build the 3DSPMK, which starts partitioning a wor...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 7 شماره 1
صفحات 97- 108
تاریخ انتشار 2019-03-01
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023