TY - JOUR
T1 - Self organizing natural scene image retrieval
AU - Serrano-Talamantes, José Félix
AU - Avilés-Cruz, Carlos
AU - Villegas-Cortez, Juan
AU - Sossa-Azuela, Juan H.
PY - 2013/6/1
Y1 - 2013/6/1
N2 - In this work we describe a new statistically-based methodology to organize and retrieve images of natural scenes by combining feature extraction, automatic clustering, automatic indexing and classification techniques. Our proposal belongs to the content-based image retrieval (CBIR) category. Our goal is to retrieve images from an image database by their content. The methodology combines randomly 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 (HSI). A K-means algorithm and a 1-NN classifier are used to build an indexed database. Three databases of images of natural scenes are used during the training and testing processes. One of the advantages of our proposal is that the images are not labeled manually for their retrieval. The performance of our framework is shown through several experimental results, including a comparison with several classifiers and comparison with related works, achieving up to 100% good recognition. Additionally, our proposal includes scene retrieval.
AB - In this work we describe a new statistically-based methodology to organize and retrieve images of natural scenes by combining feature extraction, automatic clustering, automatic indexing and classification techniques. Our proposal belongs to the content-based image retrieval (CBIR) category. Our goal is to retrieve images from an image database by their content. The methodology combines randomly 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 (HSI). A K-means algorithm and a 1-NN classifier are used to build an indexed database. Three databases of images of natural scenes are used during the training and testing processes. One of the advantages of our proposal is that the images are not labeled manually for their retrieval. The performance of our framework is shown through several experimental results, including a comparison with several classifiers and comparison with related works, achieving up to 100% good recognition. Additionally, our proposal includes scene retrieval.
KW - Content-based image retrieval (CBIR)
KW - Feature extraction
KW - Image analysis
KW - Image processing
KW - Indexed database
UR - http://www.scopus.com/inward/record.url?scp=84873172819&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2012.10.064
DO - 10.1016/j.eswa.2012.10.064
M3 - Artículo
SN - 0957-4174
VL - 40
SP - 2398
EP - 2409
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 7
ER -