Paper Details

To cope with the large amount of online videos we downloaded from YouTube, we transform every video shot intoa set of images by extracting the video shot to interpret the videos for better understanding the content that it holds. Here weconvert the video into query images and store it as testing dataset. In this phase, we make use of Gabor feature and wavelettransform on the query image cluster extracted from the query video shot V to extract the various features out of it.Visualization of the Gabor Filter for edge detection and extraction of texture for simple and complex cells. Wavelet transferuses low pass filter and high pass filter to detect the various edges and textures. Extracting the features of the training imageand these the features are stored in the array format. We extract features for the testing data set also. We further make use ofmachine learning to compare and find out perfect match of query image. The data set plays important role. In these step wemaking use of machine learning’s neural network wherein we give testing data set as in input node and compared with thenodes to give the correct output. The best matched image is then compared with the various classes in order to extract thekeyword respectevely. The keywords are then classified mainly as per the three domains namely object, event, scenes. Wethen frame a sentence using acquired keyword.

Aditya Ravindra Kale, Ankita Baburao Kate, Apurva Dinesh Kamaji, Om Arvind Kolte, Prof. S. R. Kakde, “SENTENCE FRAMING USING VIDEO”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 6, pp. 296-299, June 2018.