Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Car/Non-Car Classification in an Informative Sample Subspace
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Pedestrian Detection and Tracking for Counting Applications in Crowded Situations
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
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In this paper, we describe a general learning architecture for object detection especially car detection. In order to build such a system, we first perform dimension reduction for each example by using maximizing mutual information criterion. The algorithm directly selects projection basis from examples which can minimize Bayes error. This algorithm is named as Maximizing Mutual Information(MMI) method. Given projection basis, all of examples are projected onto these basis and then trained by Support Vector Machine(SVM). This approach can be applied to any object with distinguishable patterns. In test process, we find objects in a image by using our exhaustive search algorithm which is called a Scale based Classifier Activation Map(SCAM). We applied our detection scheme into UIUC car/non-car database [2]. In this experiment we detect 181 cars in 170 images with 200 cars. This result is competitive comparing with other papers [1, 12].