In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. The new benchmark, named Sym-PASCAL, spans challenges including object diversity, multi-objects, part-invisibility, and various complex backgrounds that are far beyond those in existing datasets. The proposed symmetry detection approach, named Side-output Residual Network (SRN), leverages output Residual Units (RUs) to fit the errors between the object symmetry groundtruth and the outputs of RUs. By stacking RUs in a deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales to ease the problems of fitting complex outputs with limited layers, suppressing the complex backgrounds, and effectively matching object symmetry of different scales. Experimental results validate both the benchmark and its challenging aspects related to realworld images, and the state-of-the-art performance of our symmetry detection approach. The benchmark and the code for SRN are publicly available at this https URL.
In the early research, symmetry extraction algorithms are qualitatively evaluated on quite limited binary shapes @cite_16 . Such shapes are selected from the MPEG-7 Shape-1 dataset for subjective observation @cite_20 . Later, Liu al @cite_13 use very a few real-world images to perform symmetry detection competitions. To be honest, SYMMAX @cite_19 could be regarded as an authentic benchmark that contains hundreds of training testing images with local symmetry annotation. But the local reflection symmetry it defined mainly focuses on low-level image edges and contours , missing the high-level concept of objects. WH-SYMMAX @cite_11 and SK506 @cite_2 are recently proposed benchmarks with annotation of object skeletons. Nevertheless, WH-SYMMAX is simply composed of side-view horses while SK506 consists objects with little background. Neither of them involves multiple objects in complex backgrounds, leaving a plenty of room for developing new object symmetry benchmarks.
@cite_19 @cite_2 @cite_16 @cite_13 @cite_20 @cite_11
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In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. The new benchmark, named Sym-PASCAL, spans challenges including object diversity, multi-objects, part-invisibility, and various complex backgrounds that are far beyond those in existing datasets. The proposed symmetry detection approach, named Side-output Residual Network (SRN), leverages output Residual Units (RUs) to fit the errors between the object symmetry groundtruth and the outputs of RUs. By stacking RUs in a deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales to ease the problems of fitting complex outputs with limited layers, suppressing the complex backgrounds, and effectively matching object symmetry of different scales. Experimental results validate both the benchmark and its challenging aspects related to realworld images, and the state-of-the-art performance of our symmetry detection approach. The benchmark and the code for SRN are publicly available at this https URL. In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. The new benchmark, named Sym-PASCAL, spans challenges including object diversity, multi-objects, part-invisibility, and various complex backgrounds that are far beyond those in existing datasets. The proposed symmetry detection approach, named Side-output Residual Network (SRN), leverages output Residual Units (RUs) to fit the errors between the object symmetry ground-truth and the outputs of RUs. By stacking RUs in a deep-to-shallow manner, SRN exploits the flow of errors among multiple scales to ease the problems of fitting complex outputs with limited layers, suppressing the complex backgrounds, and effectively matching object symmetry of different scales. Experimental results validate both the benchmark and its challenging aspects related to real-world images, and the state-of-the-art performance of our symmetry detection approach. The benchmark and the code for SRN are publicly available at https: github.com KevinKecc SRN. Local feature-based symmetry detection algorithms can simultaneously consider symmetries over all locations, scales and orientations and achieve state-of-the-art performance. This paper demonstrates the limitations of these algorithms in case of dealing with background clutters, low contrast and smooth surfaces, and presents an adaptive feature point detection algorithm to overcome those limitations. Quantitative evaluations and subjective comparisons against the state-of-the-art reflection symmetry detection algorithm on the image dataset released by "Symmetry Detection from Real World Images Competition 2013" show a significant improvement in detection accuracy and computation efficiency. Furthermore, the proposed algorithm is also tested on the non-human primates' (NHPs') video surveillance data as a preprocessing step before NHPs' behaviors analysis, and a good performance is obtained as well. Symmetry, as one of the key components of Gestalt theory, provides an important mid-level cue that serves as input to higher visual processes such as segmentation. In this work, we propose a complete approach that links the detection of curved reflection symmetries to produce symmetry-constrained segments of structures regions in real images with clutter. For curved reflection symmetry detection, we leverage on patch-based symmetric features to train a Structured Random Forest classifier that detects multiscaled curved symmetries in 2D images. Next, using these curved symmetries, we modulate a novel symmetry-constrained foreground-background segmentation by their symmetry scores so that we enforce global symmetrical consistency in the final segmentation. This is achieved by imposing a pairwise symmetry prior that encourages symmetric pixels to have the same labels over a MRF-based representation of the input image edges, and the final segmentation is obtained via graph-cuts. Experimental results over four publicly available datasets containing annotated symmetric structures: 1) SYMMAX-300 [38], 2) BSD-Parts, 3) Weizmann Horse (both from [18]) and 4) NY-roads [35] demonstrate the approach's applicability to different environments with state-of-the-art performance.