Stas Goferman Lihi Zelnik-Manor Ayellet Tal Technion

  1. Introduction  2. Principles of context-aware saliency  3. Detection of context-aware saliency  4. Result  5. Applications Outline

  Aim at detecting the image regions that represent the scene.  This definition differs from previous definitions whose goal is to either identify fixation points or detect the dominant object Context-aware saliency

 What most people think is important or salient

  1. Introduction  2. Principles of context-aware saliency  3. Detection of context-aware saliency  4. Result  5. Applications Outline

 1. Local low-level considerations, including factors such as contrast and color.  Areas that have distinctive colors or patterns should obtain high saliency  Conversely, homogeneous or blurred areas should obtain low saliency values 2. Global considerations, which suppress frequently- occurring features, while maintaining features that deviate from the norm. Four Principles(1/2)

 3.Visual organization rules, which state that visual forms may possess one or several centers of gravity about which the form is organized.  The salient pixels should be grouped together, and not spread all over the image 4.High-level factors, such as human faces.  Implemented as post-processing. Four Principles(2/2)

  1. Local low-level (b)  2. Global (c)  3. Visual organization rules about (b) + (c)  4. High-level factors (post-processing) Four principles

  1. Introduction  2. Principles of context-aware saliency  3. Detection of context-aware saliency 3.1 Local-global single-scale saliency 3.2 Multi-scale saliency enhancement 3.3 Including the immediate context 3.4 High-level factors  4. Result  5. Applications Outline

 Local-global single-scale saliency dcolor(pi,pj) is the Euclidean distance between the vectorized patches pi and pj in CIE L*a*b color space. dposition(pi,pj) is the Euclidean distance between the positions of patches pi and pj. Dissimilarity measure between a pair of patches as: Only considering the K most similar patches in the local measurement. Single-scale saliency value of pixel i at scale r is defined as:

 Multi-scale saliency enhancement Background patches are likely to have similar patches at multiple scales, Searching K most similar patches in the local measurement in scale R1 = {r,0.5r,0.25r} Representing each pixel by the set of multi- scale image patches centered at it. The saliency at pixel i is taken as the mean of its saliency at different scales

  The steps of our saliency estimation algorithm *Multiple scales  foreground *Few scales  background Steps

  1: A pixel is considered attended if its saliency value exceeds a certain threshold ( Si > 0.8).  2: The saliency of a pixel is redefined as Let d foci (i) be the Euclidean positional distance between pixel i and the closest focus of attention pixel, normalized to the range [0,1] Including the immediate context

  Final, face detection or recognized objects High-level factors face detection algorithm of [23], which generates 1 for face pixels and 0 otherwise.

  1. Introduction  2. Principles of context-aware saliency  3. Detection of context-aware saliency  4. Result  5. Applications Outline

 Result(1)

 Result(2) Comparing the saliency map in the paper with [13]. Top: Input images. Middle: the bounding boxes obtained by [13] capture a single main object. Bottom: the saliency map convey the story [13] T. Liu, J. Sun, N. Zheng, X. Tang, and H. Shum. Learning to Detect A Salient Object. In CVPR, 2007.

  1. Introduction  2. Principles of context-aware saliency  3. Detection of context-aware saliency  4. Result  5. Applications 5.1. Image retargeting 5.2. Summarization through collage creation Outline

 Seam Carving for Content-Aware Image Resizing

  Image retargeting aims at resizing an image by expanding or shrinking the non-informative regions. Image retargeting

Summarization 1.Computing the saliency maps 2.Extracting ROI by considering both the saliency and the image-edge information 3.Assemble the non- rectangular ROIs, allowing slight overlaps

 Summarization through collage creation (b)The collage summarization