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Milan Shetake
Parag Dounde
Gajanan Kale


Now a day’s digital knowledge like pictures and videos increasing too quick that causes a lot of attempt to be dedicated to develop video and image retrieval strategies, to retrieve video or image of interest. Historically text-based retrieval system is employed to retrieve video or pictures from info however this cannot be economical approach ,thus to handle this issues related to ancient system content primarily based on Content Based Image Retrieval (CBIR) and Content primarily based on Content Based Video Retrieval (CBVR) were introduced. In many applications of CBIR and CBVR system has been used, like crime hindrance, fingerprint identification, digital libraries, medicine, historical analysis, video on demand service, etc. Basically, CBIR and CBVR system tries to retrieve pictures or videos like a user-query and their goal is to retrieve similar image or video supported content properties like form, color, texture, motion. Content properties usually set into the feature vectors.In this, it arrange to implement, Methodology for CBIR supported for image classification exploitation Support Vector Machine (SVM) classifier is introduced and CBIR used C4.5 classifier. Main purpose of this approach is to slim down the search house. HSV technique for color feature, Gaussian filter technique for texture detection, Eccentricity for form, motion detection we will use background subtraction technique, KNN-tree for categorization.

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How to Cite
Milan Shetake, Parag Dounde, & Gajanan Kale. (2022). FEATURE BASED VIDEO RETRIEVAL. International Journal of Innovations in Engineering Research and Technology, 162–170. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3218