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This paper presents a new automated model-driven technique to generate test cases by using feedback from the execution of a .seed test suite. on an application under test (AUT). The test cases in the seed suite are designed to be generated automatically and executed very quickly. During their execution, feedback obtained from the AUT's run-time state is(More)
Learning based semantic video annotation is a promising approach for enabling content-based video search. However, severe difficulties, such as insufficiency of training data and curse of dimensionality, are frequently encountered. This paper proposes a novel unified scheme, Optimized Multi-Graph-based Semi-Supervised Learning (OMG-SSL), to simultaneously(More)
This paper presents an automatic video genre categorization scheme based on the hierarchical ontology on video genres. Ten computable spatio-temporal features are extracted to distinguish the different genres using a hierarchical support vector machines (SVM) classifier built by cross-validation, which consists of a series of SVM classifiers united in a(More)
This paper presents a fully automatic model-driven technique to generate test cases for graphical user interfaces (GUIs)-based applications. The technique uses feedback from the execution of a ¿seed test suite,¿ which is generated automatically using an existing structural event interaction graph model of the GUI. During its execution, the runtime effect of(More)
Graphical user interfaces (GUIs), due to their event-driven nature, present an enormous and potentially unbounded way for users to interact with software. During testing, it is important to “adequately cover” this interaction space. In this paper, we develop a new family of coverage criteria for GUI testing grounded in combinatorial(More)
This paper describes a new automated technique to generate test cases for GUIs by using <i>covering arrays</i> (CAs). The key motivation is to generate long GUI event sequences that are systematically sampled at a particular coverage strength. CAs, to date, have not been effectively used in sampling event driven systems such as GUIs which maintain state. We(More)
In this paper we discuss a typical case in video concept detection: to learn target concept using only a small number of positive samples. A novel manifold-ranking based scheme is proposed, which consists of three major components: feature pool construction, pre-filtering, and manifold-ranking. First, as there are large variations in the effective features(More)
Graph-based semi-supervised learning methods have been proven effective in tackling the difficulty of training data insufficiency in many practical applications such as video annotation. These methods are all based on an assumption that the labels of similar samples are close. However, as a crucial factor of these algorithms, the estimation of pairwise(More)
Insufficiency of labeled training data is a major obstacle for automatically annotating large-scale video databases with semantic concepts. Existing semi-supervised learning algorithms based on parametric models try to tackle this issue by incorporating the information in a large amount of unlabeled data. However, they are based on a "model assumption" that(More)
This paper describes the MSRA-USTC-SJTU experiments for TRECVID 2007. We performed the experiments in high-level feature extraction and automatic search tasks. For high-level feature extraction, we investigated the benefit of unlabeled data by semi-supervised learning, and the multi-layer (ML) multi-instance (MI) relation embedded in video by MLMI kernel,(More)