International Journal of Computer Vision
Note on free lunches and cross-validation
Neural Computation
Color image processing and applications
Color image processing and applications
Object Recognition Using Multidimensional Receptive Field Histograms
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Object detection and feature base learning with sparse convolutional neural networks
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
A biologically-inspired vision architecture for resource-constrained intelligent vehicles
Computer Vision and Image Understanding
Autonomous generation of internal representations for associative learning
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
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This work investigates the role of color in object recognition. We approach the problem from a computational perspective by measuring the performance of biologically inspired object recognition methods. As benchmarks, we use image datasets proceeding from a real-world object detection scenario and compare classification performance using color and gray-scale versions of the same datasets. In order to make our results as general as possible, we consider object classes with and without intrinsic color, partitioned into 4 datasets of increasing difficulty and complexity. For the same reason, we use two independent bio-inspired models of object classification which make use of color in different ways. We measure the qualitative dependency of classification performance on classifier type and dataset difficulty (and used color space) and compare to results on gray-scale images. Thus, we are able to draw conclusions about the role and the optimal use of color in classification and find that our results are in good agreement with recent psychophysical results.