Self organizing natural scene image retrieval

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.

► We have described a new methodology that allows the retrieval of natural images automatically. ► The methodology has been proofed for three different image databases. ► Images divided into four classes: coasts, mountains, forests, and plains. ► Extracts from the images the describing features from sets of points randomly and automatically seeded. ► The advantages of the proposal is that we do not need to manually label the image contents or areas by concepts.