A computational model of color perception and color naming
A computational model of color perception and color naming
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Is Machine Colour Constancy Good Enough?
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
The Journal of Machine Learning Research
The Journal of Machine Learning Research
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Region-Based Image Retrieval with High-Level Semantic Color Names
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Image region entropy: a measure of "visualness" of web images associated with one concept
Proceedings of the 13th annual ACM international conference on Multimedia
A discrete model for color naming
EURASIP Journal on Applied Signal Processing
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational model for color naming and describing color composition of images
IEEE Transactions on Image Processing
Learning moods and emotions from color combinations
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Discriminative compact pyramids for object and scene recognition
Pattern Recognition
International Journal of Computer Vision
A key-frame-oriented video browser
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Modulating Shape Features by Color Attention for Object Recognition
International Journal of Computer Vision
Color naming models for color selection, image editing and palette design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Special Section on CANS: Toward automatic and flexible concept transfer
Computers and Graphics
Evaluating a color-based active basis model for object recognition
Computer Vision and Image Understanding
Semantic awareness for automatic image interpretation
Proceedings of the 20th ACM international conference on Multimedia
Color constancy using single colors
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Fusing color and shape for bag-of-words based object recognition
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
Auto feature selection for object detection, can or can't?
Proceedings of the 27th Spring Conference on Computer Graphics
A naive mid-level concept-based fusion approach to violence detection in Hollywood movies
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Time matters!: capturing variation in time in video using fisher kernels
Proceedings of the 21st ACM international conference on Multimedia
Div400: a social image retrieval result diversification dataset
Proceedings of the 5th ACM Multimedia Systems Conference
Coloring Action Recognition in Still Images
International Journal of Computer Vision
Selecting semantically-resonant colors for data visualization
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labeled color chips. These color chips are labeled with color names within a well-defined experimental setup by human test subjects. However, naming colors in real-world images differs significantly from this experimental setting. In this paper, we investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labeling real-world images with color names, we use Google Image to collect a data set. Due to the limitations of Google Image, this data set contains a substantial quantity of wrongly labeled data. We propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labeled color chips for both image retrieval and image annotation.