An adaptive CBIR system for remote sensed data

Nowadays, content-based image-retrieval techniques constitute powerful tools for archiving and mining of large remote sensing image databases. However, the gap between low-level unsupervised extracted features in content-based retrieval and the high-level semantic concepts of user queries limits their performances. For that reason, we propose an adaptive content based image retrieval (CBIR) approach based on 3D-LBP (Local Binary Pattern) and HOG (Histogram of Orientated Gradients) features. The aim is to increase the performance by optimizing image features selection according to image nature (more or less textured and structured) while at the same time maintaining a small sized feature to attain better matching and lower complexity. The feature adaptation is based on two measures: a statistical measure on HOG distribution to quantify shape information and the mean range of local variances for texture measuring. Experiments demonstrate the adaptive scheme permits to reach more accuracy and better performances regarding to retrieval results and time computation.