TY  - JOUR
T1  - An Automated Approach to Retrieve Lecture Videos using Context Based Semantic Features
and Deep Learning
AU - Poornima, N. AU - Saleena, B. 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 20
SP  - 3514
EP  - 3525
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.3514.3525
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.3514.3525
KW  - Video retrieval
KW  -keyframes
KW  -features
KW  -clustering
KW  -deep learning
AB  - One of the emerging technologies in
applications like video recording and video compression
holding significant importance over the years is video
digitalization. Video retrieval is a popular research topic
and various techniques are available in literature for the
effective retrieval of videos. This research work presents
a deep learning strategy based video retrieval scheme.
Initially, the video archive is subjected for the key frame
extraction, for extracting useful keyframes from the video.
Then, the features have been extracted from the Keyframe
and formulated as the feature database. The features are
subjected for clustering using the Fuzzy C Means (FCM)
algorithm. Then, clustered features have been provided to
the deep learner for finding the optimal centroid for the
incoming user query. For the experimentation, the
research has considered videos from different category
and both the text query and the video query have been
used for the retrieval. Results from simulations
demonstrate the efficiency of the proposed deep learning
strategy in video retrieval and its achievement of
improved values of 0.98 and 0.9743, respectively for
recall, precision and F-measure.
ER  -