Regional Principal Color Based Saliency Detection
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{"title"=>"Regional principal color based saliency detection", "type"=>"journal", "authors"=>[{"first_name"=>"Jing", "last_name"=>"Lou", "scopus_author_id"=>"56421339200"}, {"first_name"=>"Mingwu", "last_name"=>"Ren", "scopus_author_id"=>"7103161440"}, {"first_name"=>"Huan", "last_name"=>"Wang", "scopus_author_id"=>"55688991300"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84911401965", "doi"=>"10.1371/journal.pone.0112475", "sgr"=>"84911401965", "pmid"=>"25379960", "issn"=>"19326203", "pui"=>"600483499"}, "id"=>"10c81897-b211-3c00-b9fe-2a198936abfe", "abstract"=>"Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms.", "link"=>"http://www.mendeley.com/research/regional-principal-color-based-saliency-detection", "reader_count"=>12, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>3, "Student > Master"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>3, "Student > Master"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>2, "Agricultural and Biological Sciences"=>1, "Design"=>1, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>4}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"United States"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1784231"], "description"=>"<p>(<b>A</b>) Regional boundaries of using the graph-based segmentation method. (<b>B</b>) Each region represented by its principal color. (<b>C</b>) Saliency map obtained with the saliency values of regional principal colors.</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232832, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.g005", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regional_principal_color_contrast_/1232832", "title"=>"Regional principal color contrast.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 03:41:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1784232"], "description"=>"<p>(<b>A</b>) Between two regions. (<b>B</b>) Between regional center and image center. (<b>C</b>) Binary segmented result simply obtained by thresholding <b>B</b> with an adaptive threshold.</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232833, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.g006", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Saliency_map_with_measuring_two_categories_of_spatial_relationships_/1232833", "title"=>"Saliency map with measuring two categories of spatial relationships.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 03:41:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1784234"], "description"=>"<p>(<b>A</b>) Original images <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Liu1\" target=\"_blank\">[20]</a>. (<b>B</b>) Ground truth <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Achanta1\" target=\"_blank\">[11]</a>. (<b>C</b>) IT <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Itti1\" target=\"_blank\">[1]</a>. (<b>D</b>) SR <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Hou1\" target=\"_blank\">[14]</a>. (<b>E</b>) FT <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Achanta1\" target=\"_blank\">[11]</a>. (<b>F</b>) CA <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Goferman1\" target=\"_blank\">[19]</a>. (<b>G</b>) RC <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Cheng1\" target=\"_blank\">[10]</a>. (<b>H</b>) Ours.</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232835, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.g007", "stats"=>{"downloads"=>4, "page_views"=>53, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Visual_results_of_our_method_compared_with_ground_truth_and_other_methods_on_dataset_MSRA_1000_/1232835", "title"=>"Visual results of our method compared with ground truth and other methods on dataset MSRA-1000.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 03:41:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1784235"], "description"=>"<p>(<b>A</b>) Precision-Recall curves. (<b>B</b>) F-measure curves. (<b>C</b>) Precision-Recall bars.</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232836, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.g008", "stats"=>{"downloads"=>4, "page_views"=>107, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantitative_comparison_on_dataset_MSRA_1000_N_A_represents_no_center_bias_/1232836", "title"=>"Quantitative comparison on dataset MSRA-1000 (N/A represents no center-bias).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 03:41:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1784237"], "description"=>"<p>(Top to bottom) Original images <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Liu1\" target=\"_blank\">[20]</a>, ground truth (GT) <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Achanta1\" target=\"_blank\">[11]</a>, color histogram similar to <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone-0112475-g003\" target=\"_blank\">Fig. 3</a><b>D</b>, global color contrast, and regional principal color based saliency detection.</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232838, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.g009", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hard_image_cases_of_our_method_in_detecting_salient_regions_/1232838", "title"=>"Hard image cases of our method in detecting salient regions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 03:41:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1784238"], "description"=>"<p>(<b>A</b>) Precision-Recall curves. (<b>B</b>) Precision-Recall bars. (<b>C</b>) F-measure curves.</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232839, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.g010", "stats"=>{"downloads"=>8, "page_views"=>158, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Uniform_quantization_vs_minimum_variance_quantization_/1232839", "title"=>"Uniform quantization vs. minimum variance quantization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 03:41:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1784240"], "description"=>"<p>(<b>A</b>)–(<b>C</b>) Varying σ from 0.01 to 1 with <i>α</i> = 0.95 and <i>δ</i> = 1/4: (<b>A</b>) Precision-Recall curves. (<b>B</b>) F-measure curves. (<b>C</b>) <i>P<sub>s</sub></i> Bars. (<b>D</b>)–(<b>F</b>) Plots of precision, recall, and F-measure for various values of σ: (<b>D</b>) <i>α</i> = 0.9, <i>δ</i> = 1/16 vs. <i>α</i> = 0.95, <i>δ</i> = 1/16. (<b>E</b>) <i>α</i> = 0.9, <i>δ</i> = 1/4 vs. <i>α</i> = 0.95, <i>δ</i> = 1/4. (<b>F</b>) <i>α</i> = 0.95, <i>δ</i> = 1/16 vs. <i>α</i> = 0.95, <i>δ</i> = 1/4.</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232841, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.g011", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantitative_comparison_for_various_combinations_of_parameters_/1232841", "title"=>"Quantitative comparison for various combinations of parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 03:41:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1784241"], "description"=>"<p>Numeric comparison on data set MSRA-1000 (%, N/A represents without center-bias).</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232842, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.t001", "stats"=>{"downloads"=>7, "page_views"=>137, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Numeric_comparison_on_data_set_MSRA_1000_N_A_represents_without_center_bias_/1232842", "title"=>"Numeric comparison on data set MSRA-1000 (%, N/A represents without center-bias).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-07 03:41:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1784242"], "description"=>"<p>Numeric comparison for various colors used in uniform quantization (%).</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232843, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.t002", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Numeric_comparison_for_various_colors_used_in_uniform_quantization_/1232843", "title"=>"Numeric comparison for various colors used in uniform quantization (%).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-07 03:41:27"}
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  • {"files"=>["https://ndownloader.figshare.com/files/1784224"], "description"=>"<p>(<b>A</b>) Minimum variance quantized <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone-0112475-g002\" target=\"_blank\">Fig. 2</a><b>A</b>. (<b>B</b>) Color histogram of the image in <b>A</b>. (<b>C</b>) Full resolution output image resulting from the retained high frequent colors. (<b>D</b>) Color histogram of <b>C</b>.</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232828, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Replacement_for_low_frequent_colors_with_minimum_variance_quantization_/1232828", "title"=>"Replacement for low frequent colors with minimum variance quantization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 03:41:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1784229"], "description"=>"<p>(<b>A</b>) Global color saliency. (<b>B</b>) Color space smoothing.</p>", "links"=>[], "tags"=>["Image segmentation", "saliency map", "computation", "Saliency Detection Saliency detection", "resolution images", "method", "color contrast", "interpolation approach", "parameters selection", "Regional Principal Color", "saliency detection algorithms", "color features", "object recognition", "quality saliency maps", "Experimental results", "saliency database show"], "article_id"=>1232830, "categories"=>["Uncategorised"], "users"=>["Jing Lou", "Mingwu Ren", "Huan Wang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112475.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Saliency_map_generated_by_global_color_contrast_/1232830", "title"=>"Saliency map generated by global color contrast.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 03:41:27"}

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Relative Metric

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