{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T13:59:34Z","timestamp":1762351174238},"reference-count":37,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Unc. Fuzz. Knowl. Based Syst."],"published-print":{"date-parts":[[2014,6]]},"abstract":"<jats:p>In this paper we present a novel associative classification algorithm for visual concept detection using interest points (color descriptors). Our algorithm relies on fuzzy (soft) partitions to generate a per-descriptor record model which is used for training associative classifiers. In this paper, we use the first 10000 images of the MIR Flickr dataset (with 38 visual concepts \u2014 14 regular annotation topics and 24 pre-annotations) for evaluating our algorithm. Because of its fuzzy nature, our approach also handles polysemy and synonymy (common problems in most crisp (non-fuzzy) image classifiers) very well. As associative classification leverages frequent patterns mined from a given dataset, its performance as adjudged from average area under curve (AUC) values over three-fold cross validation is very good, especially for regular annotation topics and for \u201cscenic\u201d concepts. Our approach has the added advantage that the rules used for classification have clear semantics, and can be comprehended easily, unlike other classifiers, such as SVM, which act as black-boxes. From an empirical perspective (on the MIR Flickr dataset), the performance of our algorithm is much better, than that of either bag-of-words (Linear SVM and SVM RBF Kernel) or Linear SVM based on fuzzy (soft) partitions\u2014both benchmark approaches are based on interest points (color descriptors).<\/jats:p>","DOI":"10.1142\/s0218488514500214","type":"journal-article","created":{"date-parts":[[2014,6,16]],"date-time":"2014-06-16T19:24:25Z","timestamp":1402946665000},"page":"429-451","source":"Crossref","is-referenced-by-count":2,"title":["A Fuzzy Associative Classification Approach to Visual Concept Detection"],"prefix":"10.1142","volume":"22","author":[{"given":"Ashish","family":"Mangalampalli","sequence":"first","affiliation":[{"name":"Centre for Data Engineering, International Institute of Information Technology (IIIT-H), Hyderabad 500032, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vikram","family":"Pudi","sequence":"additional","affiliation":[{"name":"Centre for Data Engineering, International Institute of Information Technology (IIIT-H), Hyderabad 500032, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2014,6,15]]},"reference":[{"key":"p_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888907001026"},{"key":"p_2","first-page":"645","author":"Veloso A.","year":"2006","journal-title":"ICDM"},{"key":"p_4","first-page":"4388","author":"Mangalampalli A.","year":"2010","journal-title":"ICPR"},{"key":"p_5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2009.154"},{"key":"p_6","doi-asserted-by":"publisher","DOI":"10.1080\/01969727308546046"},{"key":"p_7","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1145\/1743384.1743475","author":"Huiskes M. 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