上下文场景识别模型的稀疏贝叶斯判别学习方法-控制与决策.PDFVIP

绗?7 鍗 绗? 鏈 鎺 鍒 涓 鍐 绛 2012 骞 9 鏈 Vol. 27 No. 9 Control and Decision Sep. 2012 鏂囩珷缂栧彿: 1001-0920 (2012) 09-1320-05 涓婁笅鏂囧満鏅瘑鍒ā鍨嬬殑绋€鐤忚礉鍙舵柉鍒ゅ埆瀛︿範鏂规硶 闄 闆? 闄堝惎鍐 ( 鍚屾祹澶у鐢靛瓙涓庝俊鎭伐绋嬪闄紝涓婃捣201804) 鎽 瑕? 鍦ㄦ満鍣ㄤ汉鍦烘櫙璇嗗埆闂涓? 灏嗚繛缁満鏅殑鐩稿叧鎬ч€氳繃鍩轰簬闅愰┈灏斿彲澶ā鍨嬬殑涓婁笅鏂囨ā鍨嬭繘琛屾弿杩? 閲囩敤涓 鍚屼簬浼犵粺鐨勪娇鐢ㄧ敓鎴愭ā鍨嬫柟娉曞涔犱笂涓嬫枃鍦烘櫙璇嗗埆妯″瀷鐨勬柟寮? 棣栧厛寮曞叆绋€鐤忚礉鍙舵柉瀛︿範鏈哄涓婁笅鏂囨ā鍨嬩腑鍥惧儚鐗 寰佺殑鍚庨獙姒傜巼杩涜寤烘ā, 鐒跺悗閫氳繃璐濆彾鏂師鐞嗗皢绋€鐤忚礉鍙舵柉妯″瀷涓庨殣椹皵鍙か妯″瀷缁撳悎, 鎻愬嚭涓€绉嶈兘澶熷疄鐜颁笂涓嬫枃 鍦烘櫙璇嗗埆妯″瀷鐨勫垽鍒涔犳柟娉? 鍦ㄧ湡瀹炲満鏅暟鎹簱涓婄殑瀹為獙缁撴灉琛ㄦ槑, 鐢辫鏂规硶寰楀埌鐨勪笂涓嬫枃鍦烘櫙璇嗗埆绯荤粺鍏锋湁寰 濂界殑鍦烘櫙璇嗗埆鑳藉姏鍜屾硾鍖栫壒鎬? 鍏抽敭璇岤 鍦烘櫙璇嗗埆锛涗笂涓嬫枃妯″瀷锛涚█鐤忚礉鍙舵柉瀛︿範锛涢殣椹皵鍙か妯″瀷 涓浘鍒嗙被鍙耳 TP24 鏂囩尞鏍囧織鐮? A Sparse Bayesian discriminative learning method for context-based scene recognition model CHEN Lei, CHEN Qi-jun (School of Electronics and Information Engineering锛孴ongji University 锛孲hanghai 201804锛孋hina 锛嶤orrespondent: CHEN Qi-jun锛孍-mail 锛歲jchen@mail.tongji.edu.cn) Abstract 锛氾細锛欶or the robotics scene recognition problem, the relationship between continuous scenes can be modeled by a hidden Markov model based context model. Unlike traditionally used generative method to learn this model, a sparse Bayesian learning machine is adopted to model the posterior probabilities of image features. Then by combining the sparse model with the hidden Markov model using Bayes theory, a discriminative learning method of the context scene recognition model is proposed. The experiments on a real scene database show that the obtained scene recognition system possesses good recognition performance and generalization ability. Key words 锛氾細锛歴cene recognition锛沜ontext model 锛泂parse Bayesian learning锛沨idden Markov model 1 寮曞紩寮 瑷€瑷€瑷€ 鑰岃█, 杩欑鍋囪鏄剧劧涓嶇鍚堜汉浠