Colour and orientation of pixel based video retrieval using IHBM similarity measure

Abstract

Content-based video retrieval (CBVR) is the most energetic and stimulating research area since the early twentieth century in the domain of multimedia technology and immense quantity of retrieval techniques are introduced frequently. However, majority of the existent CBVR systems do not always give accurate results for all kinds of video databases with different colour, shape and texture feature descriptors. Sometimes, the images or videos that look similar are not semantically similar. Consequently, the retrieval outcomes that are solely centred on low level feature extraction are chiefly unsatisfactory and also unpredictable. This unlocks a new era for the research community to deviate the existent methodologies to new paradigm or direction that there is something at the back of the visual features which require to be regarded for precise searching and also retrieval. A novel CBVR methodology is suggested here centred on the selection of edge gradient feature descriptors known as HOG (Histograms of Oriented Gradients). HOG computes the edge gradient of the whole image, determines the orientation of every pixel and generates the histograms. Formerly, these extracted relevant histograms are utilized to retrieve the pertinent video frame as of the video sequence database through Integrated Histogram Bin Matching (IHBM) similarity measure. The Experimental Result of the proposed approach showed that the number of relevant retrieved video data samples is higher when compared to the existing HI Based CBVR system. The F1-score value is also high which in turn infers that the proposed approach’s performance is better when matched other existing approaches.