{"id":2404,"date":"2019-06-06T16:40:02","date_gmt":"2019-06-06T13:40:02","guid":{"rendered":"https:\/\/imagga.com\/blog\/?p=2404"},"modified":"2019-06-06T16:38:19","modified_gmt":"2019-06-06T13:38:19","slug":"imagga-among-top-performers-image-recognition-apis-comparison","status":"publish","type":"post","link":"https:\/\/imagga.com\/blog\/imagga-among-top-performers-image-recognition-apis-comparison\/","title":{"rendered":"Imagga Among the Top Performers in Recent Comparison of Image Recognition APIs"},"content":{"rendered":"<p>Image understanding relies heavily on accurate multi-label classification, which has significantly improved with the appearance of deep learning technologies. Researchers from the Department of Software and Information System Engineering Ben-Gurion University of the Negev, Israel evaluated and compared 10 of the most prominent publicly available APIs for deep learning multi-label image classification, i.e. image object classification, \u00a0in a best-of-breed challenge.<sup>1<\/sup> These include Imagga API, Watson IBM Visual Recognition API, Clarifai API, Microsoft \u00a0Computer Vision API, Wolfram Alpha Image Identification API, Google Cloud Vision API, as well as several open-source frameworks with the capability of image classification, such as Caffe, DeepDetect, OverFeat, and TensorFlow.<\/p>\n<p>We are proud to share the results of this independent study by the Ben-Gurion University team, \u00a0as Imagga made it to the top four performing APIs together with Microsoft\u2019s Computer Vision, TensorFlow and IBM\u2019s Visual Recognition.<\/p>\n<p>The evaluation was performed on 1000 images of the Visual Genome benchmark dataset, using 12 well recognized similarity metrics and an additional semantic similarity metric allowed deeper insights for comparison. These metrics evaluate the prediction performance of the APIs based on whether a predicted label exists in the ground truth label or how semantically close it is to it. Three APIs outperformed the rest when evaluating the APIs labels\u2019 predictions with these well-known metrics: Microsoft\u2019s CV, IBM\u2019s, and Imagga\u2019s APIs.<\/p>\n<p>The authors of the paper concluded, that if one is looking for a solution able to handle with high recall and precision a dataset with as many predicted labels as possible, including several which might not relevant (false positives), the <a href=\"https:\/\/imagga.com\/\">Imagga API<\/a> should be considered as top choice.<\/p>\n<p><img class=\"alignnone size-full wp-image-2405\" src=\"https:\/\/imagga.com\/blog\/wp-content\/uploads\/2019\/05\/table-1.png\" alt=\"Semantic Image Comparison Imagga\" width=\"723\" height=\"280\" \/><\/p>\n<p><img class=\"alignnone size-full wp-image-2406\" src=\"https:\/\/imagga.com\/blog\/wp-content\/uploads\/2019\/05\/table-2.png\" alt=\"Semantic Image Comparison Imagga table 2\" width=\"731\" height=\"271\" \/><\/p>\n<p><img class=\"alignnone size-full wp-image-2407\" src=\"https:\/\/imagga.com\/blog\/wp-content\/uploads\/2019\/05\/table-3.png\" alt=\"Semantic Comparison of State-of-the-Art Deep Learning Methods for Image Multi-Label Classification\" width=\"725\" height=\"268\" \/><\/p>\n<p><img class=\"alignnone size-full wp-image-2408\" src=\"https:\/\/imagga.com\/blog\/wp-content\/uploads\/2019\/05\/table-4.png\" alt=\"Semantic Comparison of State-of-the-Art Deep Learning Methods for Image Multi-Label Classification table 4\" width=\"726\" height=\"270\" \/><\/p>\n<p><img class=\"alignnone size-full wp-image-2409\" src=\"https:\/\/imagga.com\/blog\/wp-content\/uploads\/2019\/05\/table-5.png\" alt=\"Semantic Comparison of State-of-the-Art Deep Learning Methods for Image Multi-Label Classification table 5\" width=\"738\" height=\"367\" \/><\/p>\n<p>&nbsp;<\/p>\n<p>PAPER REFERENCE<\/p>\n<p><sup>1<\/sup>Adam Kubany, Shimon Ben Ishay, Ruben-sacha Ohayon, Armin Shmilovici, Lior Rokach, Tomer<br \/>\nDoitshman, April 2019. Semantic Comparison of State-of-the-Art Deep Learning Methods for Image Multi-Label Classification.\u00a0<a href=\"https:\/\/arxiv.org\/abs\/1903.09190v2\"> <strong>arXiv:1903.09190v2<\/strong><\/a><\/p>\n<p><a href=\"https:\/\/arxiv.org\/abs\/1903.09190\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"font-weight: 400;\">Read full paper here<\/span><\/a><\/p>\n<p>Imagga image recognition API features <a href=\"https:\/\/imagga.com\/solutions\/auto-tagging.html\">image tagging<\/a>, <a href=\"https:\/\/imagga.com\/solutions\/categorization-api.html\">image categorization<\/a>, <a href=\"https:\/\/imagga.com\/solutions\/cropping-api.html\">smart cropping<\/a> and <a href=\"https:\/\/imagga.com\/solutions\/visual-search.html\">visual search<\/a>. The technology can be delivered as a Cloud API or\/and an on-Premise solution.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Image understanding relies heavily on accurate multi-label classification, which has significantly improved with the appearance of deep learning technologies. Researchers [&hellip;]<\/p>\n","protected":false},"author":16,"featured_media":2426,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[44],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Imagga Among the Top Performers in Recent Comparison of Image Recognition APIs - Imagga Blog<\/title>\n<meta name=\"description\" content=\"Imagga made it to the top four performing image recognition APIs together with Microsoft\u2019s Computer Vision, TensorFlow and IBM\u2019s Visual Recognition.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/imagga.com\/blog\/imagga-among-top-performers-image-recognition-apis-comparison\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Imagga Among the Top Performers in Recent Comparison of Image Recognition APIs - 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