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Context-dependence in human cognition process is a well-established fact.Following this, we introduced the image segmentation method that can usecontext to classify a pixel on the basis of its membership to a particularobject-class of the concerned image. In the broad methodological steps, eachpixel was defined by its context window (CW) surrounding it the size of whichwas fixed heuristically. CW texture defined by the intensities of its pixelswas convoluted with weights optimized through a non-parametric functionsupported by a backpropagation network. Result of convolution was used toclassify them. The training data points (i.e., pixels) were carefully chosen toinclude all variety of contexts of types, i) points within the object, ii)points near the edge but inside the objects, iii) points at the border of theobjects, iv) points near the edge but outside the objects, v) points near or atthe edge of the image frame. Moreover the training data points were selectedfrom all the images within image-dataset. CW texture information for 1000pixels from face area and background area of images were captured, out of which700 CWs were used as training input data, and remaining 300 for testing. Ourwork gives the first time foundation of quantitative enumeration of efficiencyof image-segmentation which is extendable to segment out more than 2 objectswithin an image.

Non-parametric convolution based image-segmentation of ill-posed objects applying context window approach