Abstract
We present a content-based retrieval method for unconstrained video. To achieve the goal content based video retrieval we segmented the videos, detecting objects that match the human-defined interest. Object detection and classification is basic task in video analysis. For object detection we separate the foreground object from background and perform localization on each extracted frames from videos, and measure intensity histogram of 8-oriented frames and then perform haar-cascade, Gabor filter, active appearance model (AAM) and Convolutional Neural Network (CNN) algorithms get the authentic result. We used two datasets: Youtube and SegTek containing more than 2000 videos and using cluster computing to get the state-of-the-arts result of object detection and segmentations.
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