Unsupervised Image Retrieval with Similar Lighting Conditions

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.