Object segmentation in still camera videos

Abstract

The goal of object segmentation is to simplify and change the representation of an image into more meaningful so that it can easily analyse. Segmentation is the process of partitioning the digital image into multiple segments (set of pixels). It is the foremost step before performing other operations like recognition, scene understanding, tracking, etc. Main purpose of video segmentation is to extract the objects of interest from a series of consecutive video frames. For example surveillance video requires high-level image understanding and scene interpretation for tracking the special events. Another example is of segmenting flower from an image and video in which there are variety of flowers, the variability within a particular flower, and the variability of the imaging conditions – lighting, pose, etc. There are various approaches for segmenting the object from an image. Some of them are histogram based approach, region based approach and graph partitioning approach. In graph partitioning approach, the image being segmented is modelled as a weighted, undirected graph. Each pixel is represented as a node in the graph, and an edge is formed between every pair of pixels. The weight of an edge is a measure of the similarity between the pixels. Some popular algorithms of graph partitioning category are random walker, minimum mean cut, minimum spanning tree-based algorithm and normalized cut. In graph partitioning approach, the normalized cut algorithm is used to solve the grouping problem. In this algorithm, image is partitioned into disjoint sets by removing the edges connecting the segments. The partition can be done by finding the splitting point. The optimal solution of the splitting point is computed by solving the Eigen value problem. The optimal partitioning of the graph is the one that minimizes the weights of the edges that were removed. A normalized cut criterion measures the dissimilarity between the different groups as well as total similarity within the groups. Here group size doesn’t matter for normalized cut criterion. The normalized cut can be computed using three different splitting points and the result is analysed accordingly. A common approach for detecting the object from a video is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model and then apply the segmentation algorithm to that video. Here background subtraction has been done by frame difference method. In this method previous frame is subtracted from the current frame and difference is compared with the specific threshold value. For experimental purpose, videos of different flowers and movement of the tennis balls have been taken. All the experiments have been performed on both gray scale image and videos in MATLAB.