Scale and object aware image retargeting for thumbnail browsing

By J. Sun, Haibin Ling. 2011

86 citations recorded by the source corpus.

Many image retargeting algorithms, despite aesthetically carving images smaller, pay limited attention to image browsing tasks where tiny thumbnails are presented. When applying traditional retargeting methods for generating thumbnails, several important issues frequently arise, including thumbnail scales, object completeness and local structure smoothness. To address these issues, we propose a novel image retargeting algorithm, Scale and Object Aware Retargeting (SOAR), which has four components: (1) a scale dependent saliency map to integrate size information of thumbnails, (2) objectness (Alexe et al. 2010) for preserving object completeness, (3) a cyclic seam carving algorithm to guide continuous retarget warping, and (4) a thin-plate-spline (TPS) retarget warping algorithm that champions local structure smoothness. The effectiveness of the proposed algorithm is evaluated both quantitatively and qualitatively. The quantitative evaluation is conducted through an image browsing user study to measure the effectiveness of different thumbnail generating algorithms, followed by the ANOVA analysis. The qualitative study is performed on the RetargetMe benchmark dataset. In both studies, SOAR generates very promising performance, in comparison with state-of-the-art retargeting algorithms.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Learning Open-World Object Proposals without Learning to Classify (2021, background): “They have become the integral pre-processing steps for many computer vision systems, including object detection [33,41,42,45,59,74], segmentation [3,8,13], object discovery [12, 16, 47], weakly supervised object detection [5, 21, 53], visual tracking [35, 63], content-aware retargeting [51], etc.”
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection (2017, background): “As a pre-processing step in computer vision, saliency detection has shown a great success in ranges of visual applications, e.g . object retargeting [7, 38, 40], scene classiﬁcation [35, 33], visual tracking [4, 29], image retrieval [14, 11] and semantic segmentation [8].”
Learning Uncertain Convolutional Features for Accurate Saliency Detection (2017, methodology): “As a preprocessing procedure in computer vision, saliency detection has greatly benefited many practical applications such as object retargeting [6, 40], scene classification [37], semantic segmentation [34] and visual tracking [29, 14].”
Salient Region Detection by UFO: Uniqueness, Focusness and Objectness (2013, methodology): “The objectness is later used popularly in various vision tasks such as object detection [6] and image retargeting [32].”
BING: Binarized normed gradients for objectness estimation at 300fps (2014, unknown): “A generic objectness measure has great potential to be used as a pre-filter for many vision tasks, including object detection [3–5], visual tracking [6, 7], object discovery [8, 9], semantic segmentation [10, 11], content aware image retargeting [12], and action recognition [13].”
Prime Object Proposals with Randomized Prim's Algorithm (2013, background): “In a similar spirit, recent work has explored applications in object discovery [18], weakly supervised learning of object interaction with humans [19] or with other objects [5], as well as action recognition in still images [22] and content-aware media re-targeting [26].”
Geodesic Saliency Using Background Priors (2012, background): “This new trend is motivated by the increasing popularity of salient object based vision applications, such as object aware image retargeting [14, 15], image cropping for browsing [16], object segmentation for image editing [17] and recognition [18].”
Background Prior-Based Salient Object Detection via Deep Reconstruction Residual (2015, background): “SALIENT object detection aiming to discover the most important and informative parts in an image is gaining intensive research attention recently as it can serve as a base for a large number of multimedia applications such as image resizing, image montage, action analysis, and visual recognition [1]”
Saliency detection via Cellular Automata (2015, methodology): “As a pre-processing procedure, saliency detection can be used for many vision tasks, such as visual tracking [26], object retargeting [11, 39], image categorization [37] and image segmentation [35].”
Measuring the Objectness of Image Windows (2011, unknown): “Content-aware image resizing [5], [48].”
Contextual Hypergraph Modeling for Salient Object Detection (2013, methodology): “From left to right: images, our results, results of [18].”