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There is an increasing requirement for efficient image retargeting techniquesto adapt the content to various forms of digital media. With rapid growth ofmobile communications and dynamic web page layouts, one often needs to resizethe media content to adapt to the desired display sizes. For various layouts ofweb pages and typically small sizes of handheld portable devices, theimportance in the original image content gets obfuscated after resizing it withthe approach of uniform scaling. Thus, there occurs a need for resizing theimages in a content aware manner which can automatically discard irrelevantinformation from the image and present the salient features with moremagnitude. There have been proposed some image retargeting techniques keepingin mind the content awareness of the input image. However, these techniquesfail to prove globally effective for various kinds of images and desired sizes.The major problem is the inefficiency of these algorithms to process theseimages with minimal visual distortion while also retaining the meaning conveyedfrom the image. In this dissertation, we present a novel perspective forcontent aware image retargeting, which is well implementable in real time. Weintroduce a novel method of analysing semantic information within the inputimage while also maintaining the important and visually significant features.We present the various nuances of our algorithm mathematically and logically,and show that the results prove better than the state-of-the-art techniques.

A Novel Semantics and Feature Preserving Perspective for Content Aware Image Retargeting