This paper brings out a neoteric frame of reference for visual semantic based 3d video search and retrieval applications. Newfangled 3D retrieval application spotlight on shape analysis like object matching, classification and retrieval not only sticking up entirely with video retrieval. In this ambit, we delve into 3D-CBVR (Content Based Video Retrieval) concept for the first time. For this purpose, we intent to hitch on BOVW and Mapreduce in 3D framework. Instead of conventional shape based local descriptors, we tried to coalesce shape, color and texture for feature extraction. For this purpose, we have used combination of geometric & topological features for shape and 3D co-occurrence matrix for color and texture. After thriving extraction of local descriptors, TB-PCT (Threshold Based- Predictive Clustering Tree) algorithm is used to generate visual codebook and histogram is produced. Further, matching is performed using soft weighting scheme with L2 distance function. As a final step, retrieved results are ranked according to the Index value and acknowledged to the user as a feedback .In order to handle prodigious amount of data and Efficacious retrieval, we have incorporated HDFS in our Intellection. Using 3D video dataset, we future the performance of our proposed system which can pan out that the proposed work gives meticulous result and also reduce the time intricacy.

中文翻译：

---

使用HDFS的基于视觉语义的3D视频检索系统。

本文为基于视觉语义的3d视频搜索和检索应用程序提供了新的参考框架。新型3D检索应用程序将重点放在形状分析（例如对象匹配，分类和检索）上，而不仅仅是与视频检索紧密结合。在此范围内，我们首次研究了3D-CBVR（基于内容的视频检索）概念。为此，我们打算在3D框架中使用BOVW和Mapreduce。代替传统的基于形状的局部描述符，我们尝试合并形状，颜色和纹理以进行特征提取。为此，我们将几何和拓扑特征用于形状，并将3D共现矩阵用于颜色和纹理。在成功提取本地描述符之后，TB-PCT（基于阈值的预测聚类树）算法用于生成可视码本，并生成直方图。此外，使用具有L2距离功能的软加权方案进行匹配。最后，根据Index值对检索到的结果进行排名，并作为反馈确认给用户。为了处理大量数据和有效检索，我们将HDFS合并到了Intellection中。使用3D视频数据集，我们可以对拟议系统的性能进行未来评估，结果可以证明拟议的工作给出了细致的结果，同时也减少了时间的复杂性。检索到的结果将根据Index值进行排名，并作为反馈确认给用户。为了处理大量数据和有效检索，我们将HDFS纳入了Intellection。使用3D视频数据集，我们可以对拟议系统的性能进行未来评估，结果可以证明拟议的工作给出了细致的结果，同时也减少了时间的复杂性。检索到的结果将根据Index值进行排名，并作为反馈确认给用户。为了处理大量数据和有效检索，我们将HDFS纳入了Intellection。使用3D视频数据集，我们可以对拟议系统的性能进行未来评估，结果可以证明拟议的工作给出了细致的结果，并且减少了时间复杂度。