Association and Temporal Rule Mining for Post-Filtering of Semantic Concept Detection in Video

Liu, Ken-Hao; Weng, Meng–Tzu; Tseng, Chi-Yao; Chuang, Yung-Yu; Chen, Ming-Syan

doi:10.1109/tmm.2007.911826

Cited by 70 publications

(61 citation statements)

References 23 publications

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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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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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“…An event in this context is usually broader in scope than the events we want to recognize in wedding videos in this paper. In the multimedia research community, most of the works focus on concept detection like in (Liu et al, 2008), (Yang et al, 2007), (Snoek et al, 2006) rather than event detection. A concept detection task is different from event detection as a concept can be defined as any object or specific configuration of objects.…”

Section: Related Workmentioning

confidence: 99%

Key Event Detection in Video using ASR and Visual Data

Shrestha¹,

Venkitasubramanian²,

Moens³

2014

Proceedings of the Third Workshop on Vision and Language

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Multimedia data grow day by day which makes it necessary to index them automatically and efficiently for fast retrieval, and more precisely to automatically index them with key events. In this paper, we present preliminary work on key event detection in British royal wedding videos using automatic speech recognition (ASR) and visual data. The system first automatically acquires key events of royal weddings from an external corpus such as Wikipedia, and then identifies those events in the ASR data. The system also models name and face alignment to identify the persons involved in the wedding events. We compare the results obtained with the ASR output with results obtained with subtitles. The error is only slightly higher when using ASR output in the detection of key events and their participants in the wedding videos compared to the results obtained with subtitles.

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“…Recently, several methods [11], [17], [20], [49], [50] have been presented to exploit spatial and/or temporal context of concepts for video annotation. For example, Qi et al [11] employed the spatial co-occurrence of concepts in learning a shot-level multi-label classifier.…”

mentioning

confidence: 99%

“…For example, Qi et al [11] employed the spatial co-occurrence of concepts in learning a shot-level multi-label classifier. Liu et al [20] tried to boost the results of individual shots by applying spatial association and temporal filtering rules to a post-process over the initial annotation results. In essence, these methods work on individual shots and the correlations of concepts from neighboring shots have not been fully utilized in the learning phase of concept detectors.…”

mentioning

confidence: 99%

Sequence Multi-Labeling: A Unified Video Annotation Scheme With Spatial and Temporal Context

Li¹,

Duan

Yang³

et al. 2010

IEEE Trans. Multimedia

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Abstract-Automatic video annotation is a challenging yet important problem for content-based video indexing and retrieval. In most existing works, annotation is formulated as a multi-labeling problem over individual shots. However, video is by nature informative in spatial and temporal context of semantic concepts. In this paper, we formulate video annotation as a sequence multi-labeling (SML) problem over a shot sequence. Different from many video annotation paradigms working on individual shots, SML aims to predict a multi-label sequence for consecutive shots in a global optimization manner by incorporating spatial and temporal context into a unified learning framework. A novel discriminative method, called sequence multi-label support vector machine (SVM SML ), is accordingly proposed to infer the multi-label sequence for a given shot sequence. In SVM SML , a joint kernel is employed to model the feature-level and concept-level context relationships (i.e., the dependencies of concepts on the low-level features, spatial and temporal correlations of concepts). A multiple-kernel learning (MKL) algorithm is developed to optimize the kernel weights of the joint kernel as well as the SML score function. To efficiently search the desirable multi-label sequence over the large output space in both training and test phases, we adopt an approximate method to maximize the energy of a binary Markov random field (BMRF). Extensive experiments on TRECVID'05 and TRECVID'07 datasets have shown that our proposed SVM SML gains superior performance over the state-of-the-art.

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Association and Temporal Rule Mining for Post-Filtering of Semantic Concept Detection in Video

Cited by 70 publications

References 23 publications

Model Selection for Poisson Regression via Association Rules Analysis

Model Selection for Poisson Regression via Association Rules Analysis

Key Event Detection in Video using ASR and Visual Data

Sequence Multi-Labeling: A Unified Video Annotation Scheme With Spatial and Temporal Context

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