TY - GEN
T1 - Unsupervised image retrieval with similar lighting conditions
AU - Serrano, J. Félix
AU - Avilés, Carlos
AU - Sossa, Humberto
AU - Villegas, Juan
AU - Olague, Gustavo
PY - 2010
Y1 - 2010
N2 - In this work a new method to retrieve images with similar lighting conditions is presented. It is based on automatic clustering and automatic indexing. Our proposal belongs to Content Based Image Retrieval (CBIR) category. The goal is to retrieve from a database, images (by their content) with similar lighting conditions. When we look at images taken from outdoor scenes, much of the information perceived depends on the lighting conditions. The proposal combines fixed and random extracted points for feature extraction. The describing features are the mean, the standard deviation and the homogeneity (from the co-occurrence matrix) of a sub-image extracted from the three color channels: (H, S, I). A K-MEANS algorithm and a 1-NN classifier are used to build an indexed database of 300 images in order to retrieve images with similar lighting conditions applied on sky regions such as: sunny, partially cloudy and completely cloudy. One of the advantages of the proposal is that we do not need to manually label the images for their retrieval. The performance of our framework is demonstrated through several experimental results, including the improved rates for images retrieval with similar lighting conditions. A comparison with another similar work is also presented.
AB - In this work a new method to retrieve images with similar lighting conditions is presented. It is based on automatic clustering and automatic indexing. Our proposal belongs to Content Based Image Retrieval (CBIR) category. The goal is to retrieve from a database, images (by their content) with similar lighting conditions. When we look at images taken from outdoor scenes, much of the information perceived depends on the lighting conditions. The proposal combines fixed and random extracted points for feature extraction. The describing features are the mean, the standard deviation and the homogeneity (from the co-occurrence matrix) of a sub-image extracted from the three color channels: (H, S, I). A K-MEANS algorithm and a 1-NN classifier are used to build an indexed database of 300 images in order to retrieve images with similar lighting conditions applied on sky regions such as: sunny, partially cloudy and completely cloudy. One of the advantages of the proposal is that we do not need to manually label the images for their retrieval. The performance of our framework is demonstrated through several experimental results, including the improved rates for images retrieval with similar lighting conditions. A comparison with another similar work is also presented.
UR - http://www.scopus.com/inward/record.url?scp=78149480546&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.1062
DO - 10.1109/ICPR.2010.1062
M3 - Contribución a la conferencia
AN - SCOPUS:78149480546
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 4368
EP - 4371
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -