Abstract-Automatic semantic concept detection in video is important for effective content-based video retrieval and mining and has gained great attention recently. In this paper, we propose a general post-filtering framework to enhance robustness and accuracy of semantic concept detection using association and temporal analysis for concept knowledge discovery. Co-occurrence of several semantic concepts could imply the presence of other concepts. We use association mining techniques to discover such inter-concept association relationships from annotations. With discovered concept association rules, we propose a strategy to combine associated concept classifiers to improve detection accuracy. In addition, because video is often visually smooth and semantically coherent, detection results from temporally adjacent shots could be used for the detection of the current shot. We propose temporal filter designs for inter-shot temporal dependency mining to further improve detection accuracy. Experiments on the TRECVID 2005 dataset show our post-filtering framework is both efficient and effective in improving the accuracy of semantic concept detection in video. Furthermore, it is easy to integrate our framework with existing classifiers to boost their performance.

Background: Retroperitoneal lymphangiomatosis (RL) is a rare form of primary lymphedema featuring aberrant retroperitoneal lymphatic proliferation. It causes recurrent cellulitis, repeated interventions, and poor life quality. This study aimed to investigate proper diagnositc criteria and surgical outcomes for RL with extremity lymphedema. Methods: Between 2012 and 2018, 44 primary lower-extremity lymphedema cases received lymphoscintigraphy, magnetic resonance imaging, and single-photon electron computed tomography to detect RL. RL patients underwent vascularized lymph node transfers (VLNT) for extremity lymphedema and intra-abdominal side-toend chylovenous bypasses (CVB) for chylous ascites. Complications, CVB patency, and quality of life were evaluated postoperatively. Results: Six RL patients (mean age of 30.3 years) had chylous ascites with five had lower-extremity lymphedema. All CVBs remained patent, though one required reanastomosis, giving a 100% patency rate. Four unilateral and one bilateral extremity lymphedema underwent six VLNTs with 100% flap survival. Patients reported improved quality of life (P = 0.023), decreased cellulitis incidence (P = 0.041), and improved mean lymphedema circumference (P = 0.043). All patients resumed a normal diet and activity. Conclusions: Evaluating primary lower-extremity lymphedema patients with MRI and SPECT could reveal a 13.6% prevalence of RL and guide treatment of refractory extremity lymphedema. Intra-abdominal CVB with VLNT effectively treated RL with chylous ascites and extremity lymphedema.

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Due to the dynamic nature of data streams, a sliding window is used to generate synopses that approximate the most recent data within the retrospective horizon to answer queries or discover patterns. In this paper, we propose a dynamic scheme for wavelet synopses management in sensor networks. We define a data structure Sliding Dual Tree, abbreviated as SDT, to generate dynamic synopses that adapts to the insertions and deletions in the most recent sliding window. By exploiting the properties of Haar wavelet transform, we develop several operations to incrementally maintain SDT over consecutive time windows in a time-and spaceefficient manner. These operations directly operate on the transformed time-frequency domain without the need of storing/ reconstructing the original data. As shown in our thorough analysis, our SDT-based approach greatly reduces the required resources for synopses generation and maximizes the storage utilization of wavelet synopses in terms of the window length and quality measures. We also show that the approximation error of the dynamic wavelet synopses, i.e., L 2 -norm error, can be incrementally updated. We also derive the bound of the overestimation of the approximation error due to the incremental thresholding scheme. Furthermore, the synopses can be used to answer various kinds of numerical queries such as point and distance queries. In addition, we show that our SDT can adapt to resource allocation to further enhance the overall storage utilization over time. As demonstrated by our experimental results, our proposed framework can outperform current techniques in both real and synthetic data.

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