Video semantic indexing using object detection-derived features
Kotaro Kikuchi, Kazuya Ueki, Tetsuji Ogawa, Tetsunori Kobayashi · 2016
A new feature extraction method based on object detection to achieve accurate and robust semantic indexing of videos is proposed. Local features (e.g., SIFT and HOG) and convolutional neural network (CNN)-derived features, which have been used in semantic indexing, in general are extracted from the entire image and do not explicitly represent the information of meaningful objects that contributes to the determination of semantic categories. In this case, the background region, which does not contain the meaningful objects, is unduly considered, exerting a harmful effect on the indexing performance. In the present study, an attempt was made to suppress the undesirable effects derived from the redundant background information by incorporating object detection technology into semantic indexing. In the proposed method, a combination of the meaningful objects detected in the video frame image is represented as a feature vector for verification of semantic categories. Experimental comparisons demonstrate that the proposed method facilitates the TRECVID semantic indexing task.