Image Auto-Cropping using Star-Light and Color Saturation

• Main Authors: Tung-Cheng Chen, 陳東承
• Other Authors: Chen-Chiung Hsieh
• Format: Others
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/98590937943917974949

碩士 === 大同大學 === 資訊工程學系(所) === 103 === Automatic image cropping can be used to segment the main theme of an image on different platforms such as mobile phones, cameras, and web pages. For the same input image the results are different in various systems with the same function. Some of the results may even be completely different. However, we should not say the result is wrong because it involves personal sense of aesthetics. Different methods may focus on different topics like human faces or color contrast. Ma and Guo assessed regions based on entropy, size, and distance from the image center. Zhang et al. used face detection to find regions of interest. Yen and Lin used training set before auto-cropping. Cheng used color contrast to generate a saliency map for cropping. Focused object is usually the saliency regions in an image. Performance of edge detection are often the measurement of saliency map. Therefore, we use L*a*b color space to get color saturation value first and then use star-light mask to compare the pixel color saturation to generate an edge similar saliency map. Finally, calculate the pixel value for image cropping. Unlike ordinary well known Canny edges or Sobel edges, our method can generate better saliency map in which edges are kept while background edges are removed. Usability and reliability are higher than face-based detection or color contrast based approaches. In addition, we propose two algorithms to automatically crop the main theme from saliency map. Our algorithm is both simple and easy to understand. It is an creative development approach compared with other auto-cropping method.