Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.

[1]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Sergio Guadarrama,et al.  Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Christoph H. Lampert,et al.  Beyond sliding windows: Object localization by efficient subwindow search , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Zhiling Wang,et al.  Visual Tracking Model Based on Feature-Imagination and Its Application , 2010, 2010 International Conference on Multimedia Information Networking and Security.

[5]  Simone Frintrop,et al.  VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search , 2006, Lecture Notes in Computer Science.

[6]  Nicholas A. Steinmetz,et al.  Top-down control of visual attention , 2010, Current Opinion in Neurobiology.

[7]  Antonio Torralba,et al.  Top-down control of visual attention in object detection , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[8]  Florent Perronnin,et al.  A similarity measure between unordered vector sets with application to image categorization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Robert Sekuler,et al.  Visual Memory Decay Is Deterministic , 2005, Psychological science.

[10]  Sabine Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Yael Pritch,et al.  Saliency filters: Contrast based filtering for salient region detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  P. Downing,et al.  Interactions Between Visual Working Memory and Selective Attention , 2000, Psychological science.

[13]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Mubarak Shah,et al.  Visual attention detection in video sequences using spatiotemporal cues , 2006, MM '06.

[17]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[18]  E. Bartha,et al.  Altered lymphocyte acetylcholinesterase activity in patients with senile dementia , 1987, Neuroscience Letters.

[19]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Cordelia Schmid,et al.  Combining efficient object localization and image classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[21]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[23]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[24]  James H. Elder,et al.  Design and perceptual validation of performance measures for salient object segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[25]  Joachim Denzler,et al.  One-Shot Learning of Object Categories Using Dependent Gaussian Processes , 2010, DAGM-Symposium.