Association Rule Mining on Remotely Sensed Images Using P-trees

Ding, Qin; Ding, Qiang; Perrizo, William

doi:10.1007/3-540-47887-6_7

Cited by 27 publications

(18 citation statements)

References 9 publications

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“…Tesic et al [28] used a data mining approach to find the spatial associations between classes of texture from aerial photos. Similarly Ding et al [29] derive association rules on Remote Sensed Imagery data using a Peano Count Tree (P-tree) structure with an extension of the more common APriori [30] algorithm. Chum et al [31] use data mining to find near duplicate images within a database of photographs, while Quack et al [10] applied Association rule data mining to object recognition by mining spatially grouped SIFT descriptors.…”

Section: Related Workmentioning

confidence: 99%

Action Recognition Using Mined Hierarchical Compound Features

Gilbert

Illingworth

Bowden

2011

IEEE Trans. Pattern Anal. Mach. Intell.

140

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Abstract-The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single frame object recognition and adapting them for temporal based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition, the features used are often engineered to fire sparsely. This is to ensure the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an over complete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learnt efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative and sparse. This results in fast, accurate recognition with real-time performance on high resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art datasets, the popular KTH dataset to provide a comparison with other state-of-the-art approaches, the Multi-KTH dataset to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 datasets, provide challenging complex actions taken from commercial movie sequences. For all four datasets, the proposed hierarchical approach out performs all other methods reported thus far in the literature, and can achieve real-time operation.

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Section: Related Workmentioning

confidence: 99%

Action Recognition Using Mined Hierarchical Compound Features

Gilbert

Illingworth

Bowden

2011

IEEE Trans. Pattern Anal. Mach. Intell.

140

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“…Tesic et al [17] use a Data mining approach to find the spatial associations between classes of texture from aerial photos. Similarly Ding et al [18] derive association rules on Remote Sensed Imagery data using a Peano Count Tree (P-tree) structure with an extension of the more common APriori [7] algorithm.…”

Section: Related Workmentioning

confidence: 99%

Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners

Gilbert

Illingworth

Bowden

2008

Lecture Notes in Computer Science

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Abstract. The use of sparse invariant features to recognise classes of actions or objects has become common in the literature. However, features are often "engineered" to be both sparse and invariant to transformation and it is assumed that they provide the greatest discriminative information. To tackle activity recognition, we propose learning compound features that are assembled from simple 2D corners in both space and time. Each corner is encoded in relation to its neighbours and from an over complete set (in excess of 1 million possible features), compound features are extracted using data mining. The final classifier, consisting of sets of compound features, can then be applied to recognise and localise an activity in real-time while providing superior performance to other state-of-the-art approaches (including those based upon sparse feature detectors). Furthermore, the approach requires only weak supervision in the form of class labels for each training sequence. No ground truth position or temporal alignment is required during training.

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“…A large number of studies use association rules analysis in a wide variety of areas: biology (Becquet, Blachon, Jeudy, Boulicaut & Gandrillon, 2002;Marghny & El-Semman, 2005), business and marketing (Changchien & Lu, 2001), geography (Appice, Ceci, Lanza, Lisi & Malerba, 2003;Ding, Ding & Perrizo, 2002;Lee, Hong, Ko, Tsao & Lin, 2007), agriculture (Matsumoto, 1998), education (Garcia, Romero, Ventura & Calders, 2007;Garcia, Amandi, Schiaffino & Campo, 2007), photography (Liu, Weng, Tseng, Chuang & Chen, 2008), economics (Dopfer & Potts, 2004;Kuo, Lin & Shih, 2007), and so on.…”

Section: Poisson Regression and Association Rules Analysismentioning

confidence: 99%

Model Selection for Poisson Regression via Association Rules Analysis

Changpetch¹,

Lin²

2015

IJSP

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This study integrates association rules analysis, a methodology for selecting potential interactions, with Poisson regression modeling. Though typically ignored in conventional Poisson regression, interactions are very common in practice. However, selecting a Poisson regression model when many main effects and interactions are involved is problematic. In this study, we develop a model selection framework to address this problem. Specifically, we focus on building an optimal Poisson regression model by (1) discretizing the response and quantitative attributes into levels; (2) exploring via association rules analysis combinations of input variables that have a significant impact on response; (3) selecting potential (low-and high-order) interactions; (4) converting these potential interactions into new variables; and (5) selecting variables from all the input variables and the newly created variables (interactions) to build the optimal Poisson regression model. Our model selection procedure is the first approach to enable a global search for potential interactions and the first to establish the optimal combination of main effects and interaction effects in the Poisson regression model. A real-life example is given for illustration. It is shown that the proposed method finds the optimal model including important interactions that cannot be found by other existing methods.

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Association Rule Mining on Remotely Sensed Images Using P-trees

Cited by 27 publications

References 9 publications

Action Recognition Using Mined Hierarchical Compound Features

Action Recognition Using Mined Hierarchical Compound Features

Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners

Model Selection for Poisson Regression via Association Rules Analysis

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