Scalable Classification in Large Scale Spatiotemporal Domains Applied to Voltage-Sensitive Dye Imaging

Vainer, Igor; Kraus, Sarit; Kaminka, Gal A.; Slovin, Hamutal

doi:10.1109/icdm.2009.24

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…In [21], Yoon apply feature selection techniques for hand gesture applications. Vainer et al [20] also present a feature selection approach for learning models that obtain accurate classification of large scale data of Voltage-Sensitive Dye Imaging. Zhao [22] propose a framework of feature extraction for classification of hand movement imagery based on both temporal and spatial features of Single-Trial EEG.…”

Section: Feature Subset Selectionmentioning

confidence: 99%

Discovering patterns in traffic sensor data

Banaei-Kashani

Shahabi

Pan

2011

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming

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We maintain a one of a kind, large-scale and high resolution (both spatially and temporally) traffic sensor dataset collected from the entire Los Angeles County road network. Traffic sensors (installed under the road pavement) are used to measure real-time traffic flows through road segments. In this paper, we exploit this dataset to rigorously verify two popular instinctive understandings about traffic flows on road segments: 1) each road segment has a typical traffic flow (known by local travelers) and one can often categorize road segments based on the similarity of their traffic flows, and 2) the road segments within each category not only have similar traffic flows but also are similar in their other characteristics (such as locality, connectivity). Toward this end, we developed a hypothesis analysis framework based on a variety of clustering and correlation evaluation techniques and leveraged this framework to respectively show the following. First, the set of road segments can indeed be partitioned into a set of distinct subpartitions with similar traffic flows, and there is a limited number of signature traffic patterns/labels each of which can accurately represent all traffic flows of a subpartition of the road segments. Second, all segments within each subpartition (represented by one signature) are also highly similar in three other characteristics, namely, direction, connectivity and locality. Our experiments verify our observations with high confidence.

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Section: Feature Subset Selectionmentioning

confidence: 99%

Discovering patterns in traffic sensor data

Banaei-Kashani

Shahabi

Pan

2011

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming

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“…In the past decade, several approaches for classification which adopt a coarse to fine strategy were proposed and successfully demonstrated [ [6]. The multiresolution classification method proposed by I. Blayavs involved a tree structure for the training process and testing process.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Classification Model Based on Granular Computing

Liu

Zhang

2010

2010 2nd International Workshop on Intelligent Systems and Applications

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In this paper, after a brief overview of the existing methods, we present a new hierarchical classification algorithm based on quotient space theory of the granular computing. This algorithm deals with the samples from coarse to fine both in the training and testing processes. A group of classifiers are firstly trained by the samples generated under different quotient space. Then the trained classifiers will be used to label the testing samples set hierarchically. In our method, Support Vector Machines is chosen to acquire the discrimination function between two classes in the training processes. And the hypercubes which represent support vectors are subdivided to generate the samples set for training and testing under different quotient space. Finally, experimental results have substantiated the effectiveness of the proposed method.

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Shadow removing in a surveillance system by a multi-resolution classification strategy

Zhang

2010

2010 Sixth International Conference on Natural Computation

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Shadow removing is a key issue for moving objects detection in a surveillance systems. However, few research address this problem by a learning strategy. In this paper, we present a multi-resolution classification method to remove shadows from the object detection result. Because the number of samples which denote the shadow and object is reasonably large, we adopt a coarse-to fine strategy during the classification process. By partitioning feature space into hypercubes according to different resolutions, we train a group of classifiers which can label the samples for testing from coarse to fine. Support Vector Machines are chosen in the process of training and the hypercubes which represent support vectors are subdivided in order to generate the sample set intended for training in a higher resolution. Because of the conglomeration property of the samples to be tested, we can label most of the samples using the simple classifiers trained at low resolution. In some cases, the method presented in this paper can reduce the computational complex of the classification algorithm. Finally, experimental results have substantiated the effectiveness of the proposed method.

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Scalable Classification in Large Scale Spatiotemporal Domains Applied to Voltage-Sensitive Dye Imaging

Cited by 3 publications

References 13 publications

Discovering patterns in traffic sensor data

Discovering patterns in traffic sensor data

A Hierarchical Classification Model Based on Granular Computing

Shadow removing in a surveillance system by a multi-resolution classification strategy

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