No abstract

The World Wide Web today has grown so wide and the video-on-demand applications and video share web are becoming very popular day-by-day on the World Wide Web. An efficient video similarity search algorithm for contentbased video retrieval is important in video-on-demand based services. However, there is no satisfying video similarity search algorithm showing cent percentage performance. It is proposed here to implement a video similarity measure algorithm based on the color-features of each video represented by a compact fixed size representation known as Video Signature. This Video signature which is based on the image signature is computed on the basis of YCbCr Histogram and the sum of its weighted means. The video signatures of videos are then used to find the similar videos in-terms of visually similar frames, by using the range. This method of similarity measure is assumed to be efficient in various aspects.

This article presents a survey of machine learning for image recognition. Image Recognition is the task of identifying objects of interest within an image and recognizing which category the image belongs to. The common goal of image recognition is the classification of detected objects into different categories. Image processing technology based on machine learning has been widely used in feature image, classification, segmentation and recognition and is a widely used in various fields. In this paper use of machine learning for image recognition is discussed.