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For assistance and to order your printed poster call PosterPresentations.com at 1.866.649.3004 Object Placeholders Use the placeholders provided below to add new elements to your poster: Drag a placeholder onto the poster area, size it, and click it to edit. Section Header placeholder Move this preformatted section header placeholder to the poster area to add another section header. Use section headers to separate topics or concepts within your presentation. Text placeholder Move this preformatted text placeholder to the poster to add a new body of text. Picture placeholder Move this graphic placeholder onto your poster, size it first, and then click it to add a picture to the poster. © 2011 PosterPresentations.com 2117 Fourth Street, Unit C Berkeley CA 94710 posterpresenter@gmail.com Student discounts are available on our Facebook page. Go to PosterPresentations.com and click on the FB icon. -The Problems (1) Model human clutter perception using proto-objects. (2) Estimate “set size” for realistic scenes. -What is Visual Clutter? A “confused collection” or a “crowded disorderly state”. Increasing visual clutter leads to poorer performance in many behavioral tasks (e.g. visual search). -What is a Set Size Effect? A drop in search performance with an increase in the number of objects [1]. However, an object count is difficult to quantify in real world scenes. -What are Proto-objects? Regions of locally similar features. They can be objects, object parts, or just pieces that come together to form objects. -What does our Clutter Model do? It segments proto-objects from an image, then counts the number of proto-objects as an estimate of visual clutter. -Clutter Model Our model successfully predicts the degree that a person will perceive an image as cluttered, and out-performs all other existing models of clutter perception. -Parametric Modelling of Earth Mover’s Distance Statistics We show that Earth Mover’s Distance statistics (EMD) follow a Weibull distribution for efficient parametric modeling. -Proto-object Segmentation Unsupervised image partitioning by our novel parametric EMD model. -Clutter Dataset We obtained a clutter ground truth by having people rank order a subset of images from SUN09 [2] from least to most cluttered. Contributions -Superpixel Graph An image is first pre-processed into superpixels using SLIC [3], then it is formulated into a graph, where the nodes are the superpixels. Each pair of adjacent nodes are connected with a weighted edge. -Edge Weights: Earth Mover’s Distance The edges are weighted by the dissimilarity between the pair of nodes, in terms of Intensity, Color, and Orientation. We use Earth Mover’s Distance as the dissimilarity distances. EMD is defined to minimize the following with an optimal flow : where and are the two signatures to be compared, and denotes some dissimilarity metric (i.e. the L2 distance) between and in. -Edge Labeling for Superpixel Clustering Each edge is labeled as Similar or Dissimilar, based on a similarity-threshold. The dissimilar edges are removed to form superpixel clusters, which are merged to form proto-objects. -Compute using Weibull-Mixture-Model EMD is identical to Mallow’s Distance,, when P and Q have the same total mass [4], and Lp-based distance statistics follow a Weibull distribution [5]. Therefore, a two- component WMM (similar/dissimilar) can be used for the computation of. -Normalized Clutter Measure The count of the final proto-objects are divided by the initial # of superpixels to produce our final clutter measure for a given image. Method Modeling Clutter Perception using Parametric Proto-object Partitioning 1 Dept of Computer Science, 2 Dept of Psychology, Stony Brook University; 3 Dept of Statistics, Penn State University Chen-Ping Yu 1, Wen-Yu Hua 3, Dimitris Samaras 1, Gregory Zelinsky 1,2 How can we quantify set size or the number of objects in these scenes? Experiments and Results at TLT Media Lab of Stony Brook University SLIC k = 1000 Superpixel Graph 0.11 0.77 0.15 0.86 0.28 0.63 0.35 0.77 0.12 0.75 0.21 0.82 0.31 0.04 0.32 0.93 0.81 0.38 0.71 0.68 0.65 0.75 0.23 0.05 0.11 0.77 0.15 0.86 0.28 0.63 0.35 0.77 0.12 0.75 0.21 0.82 0.31 0.04 0.32 0.93 0.81 0.38 0.71 0.68 0.65 0.75 0.23 0.05 = 0.6 merge proto- objects Introduction Dataset -90 800x600 real world images, sampled from the SUN Database [2] -Divided into 6 groups, each with a different range of object counts (from SUN09). -Clutter rankings (15 raters) and object segmentations (SUN) available for each image -Mean correlation between all pairs of human ranking: Spearman’s ρ = 0.6919 WMM-mleWMM-nls Mean-shift [6] Graph based [7] Power Law [8] Edge Density [9] Feature Congestion [10] # of Objects (SUN) [2] Color-cluster clutter [11] 0.8038 0.7966 0.72620.6612 0.6439 0.6231 0.5337 0.5255 0.4810 Correlations between human clutter perception and all the evaluated methods. WMM is our Weibull mixture model. Our method runs in 20 seconds using 800x600 images, on an Intel Core i7 3.0 Ghz machine with 8 Gb RAM. Intensity Color Orientation Weibull-Mixture Model (WMM): Similarity Threshold – the crossing point between the two components: References & Acknowledgment [1] J. M. Wolfe. Visual search. Attention, 1998. [2] J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. SUN database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010. [3] R. Achanta, A. Shaji, L. Smith, A. Lucchi, P. Fua, and S. Susstrunk. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI, 2012. [4] E. Levina and P. Bickel. The earth mover’s distance is the mallows distance: some insights from statistics. In ICCV, 2001. [5] G. J. Burghouts, A. W. M. Smeulders, and J.-M. Geusebroek. The distribution family of similarity distances. In NIPS, 2007. [6] D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE TPAMI, 2002. [7] P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmentation. In ICCV, 2004. [8] M. J. Bravo and H. Farid. A scale invariant measure of clutter. Jounal of Vision, 2008. [9] M. L. Mack and A. Oliva. Computational estimation of visual complexity. In the 12 th Annual Object, Perception, Attention, and Memory Conference, 2004. [10] R. Rosenholtz, Y. Li, and L. Nakano. Measuring visual clutter. Journal of Vision, 2007. [11] M. C. Lohrenz, J. G. Trafton, R. M. Beck, and M. L. Gendron. Amodel of clutter for complex, multivariate geospatial displays. Human Factors, 2009. We appreciate the authors of C3 model, Dr. Burghouts of [5], and Dr. Matthew Asher for discussions and code sharing. This work was supported by NIMH Grant R01-MH064748 to G.J.Z., NSF Grant IIS-1111047 to G.J.Z. and D.S., and the SUBSAMPLE Project of the DIGITEO Institute, France. 31~40 objects 15 images 51~60 objects 15 images 1~10 objects 15 images 90 images total