Cost-sensitive learning of top-down modulation for attentional control

被引:35
作者
Borji, Ali [1 ]
Ahmadabadi, Majid N. [1 ,2 ]
Araabi, Babak N. [1 ,2 ]
机构
[1] Inst Res Fundamental Sci, Sch Cognit Sci, Tehran, Iran
[2] Univ Tehran, Control & Intelligent Proc Ctr Excellence, Sch Elect & Comp Engn, Tehran, Iran
关键词
Selective perception; Top-down attention; Bottom-up attention; Basic saliency model; Object detection; VISUAL-ATTENTION; SIGN DETECTION; MODEL;
D O I
10.1007/s00138-009-0192-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A biologically-inspired model of visual attention known as basic saliency model is biased for object detection. It is possible to make this model faster by inhibiting computation of features or scales, which are less important for detection of an object. To this end, we revise this model by implementing a new scale-wise surround inhibition. Each feature channel and scale is associated with a weight and a processing cost. Then a global optimization algorithm is used to find a weight vector with maximum detection rate and minimum processing cost. This allows achieving maximum object detection rate for real time tasks when maximum processing time is limited. A heuristic is also proposed for learning top-down spatial attention control to further limit the saliency computation. Comparing over five objects, our approach has 85.4 and 92.2% average detection rates with and without cost, respectively, which are above 80% of the basic saliency model. Our approach has 33.3 average processing cost compared with 52 processing cost of the basic model. We achieved lower average hit numbers compared with NVT but slightly higher than VOCUS attentional systems.
引用
收藏
页码:61 / 76
页数:16
相关论文
共 31 条
[1]  
Barnes N, 2004, 2004 IEEE INTELLIGENT VEHICLES SYMPOSIUM, P566
[2]   THE LAPLACIAN PYRAMID AS A COMPACT IMAGE CODE [J].
BURT, PJ ;
ADELSON, EH .
IEEE TRANSACTIONS ON COMMUNICATIONS, 1983, 31 (04) :532-540
[3]   Control of goal-directed and stimulus-driven attention in the brain [J].
Corbetta, M ;
Shulman, GL .
NATURE REVIEWS NEUROSCIENCE, 2002, 3 (03) :201-215
[4]   Traffic sign recognition and analysis for intelligent vehicles [J].
de la Escalera, A ;
Armingol, JM ;
Mata, M .
IMAGE AND VISION COMPUTING, 2003, 21 (03) :247-258
[5]   Road traffic sign detection and classification [J].
delaEscalera, A ;
Moreno, LE ;
Salichs, MA ;
Armingol, JM .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 1997, 44 (06) :848-859
[6]  
Desimone R., 1955, ANNU REV NEUROSCI, V18, P193
[7]   Road-sign detection and tracking [J].
Fang, CY ;
Chen, SW ;
Fuh, CS .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2003, 52 (05) :1329-1341
[8]  
Frintrop S., 2006, LECT NOTES ARTIFICIA, V3899
[9]  
GAVRILA DM, 1999, MUSTERERKENNUNG DAGM
[10]  
GOMEZ LC, 2007, P IM AN REC ICIAR MO