Proc. SPIE. 5960, Visual Communications and Image Processing 2005
KEYWORDS: Visualization, Databases, Feature extraction, Signal processing, Quantization, Human vision and color perception, Image retrieval, Information visualization, RGB color model, Single crystal X-ray diffraction
In content based image retrieval similarity measurement is one of the most important aspects in a large image database for efficient search and retrieval to find the best answer for a user query. Color and texture are among the more expressive of the visual features. Considerable work has been done in designing efficient descriptors for these features for applications such as similarity retrieval. The MPEG-7 specifies a standard set of descriptors for color, texture and shape. In the <i>Human Vision System </i>(HVS), visual information is not perceived equally; some information may be more
important than other information. The purpose of this paper is to show how the MPEG-7 descriptor based on human vision system can be efficiently utilized for image matching. We considered <i>scalable color</i> <i>descriptor</i> (SCD) and <i>edge histogram descriptor </i>(EHD) descriptors of MPEG7. To increase the matching performance we used <i>Linde-Buzo-Gray </i>(LBG) vector quantization algorithm and image splitting by magnitude weighting technique for efficient use of SCD and EHD, respectively. The proposed matching method is considered to be a more efficient image content-based retrieval than EHD and SCD. Experimental results support this claim. Experiments on 3000 images from Corel Photo collections show that the proposed method yields better retrieval performance especially for semantic similarity.
Detection of changes in remotely sensed geographic images is required for a variety of applications including natural disasters. Change detection is an important process utilized for updating the geographic information system (GIS) data, monitoring natural resources and urban developments. It provides quantitative analysis of the spatial distribution of the area of interest. Different types of change detection techniques include: Multi-date visual composite (multitemporal composite), image differencing, post-classification, etc. In this work, we propose a change detection algorithm based on multiresolution analysis and motion estimation. We use multispectral satellite imagery and apply the dual tree complex wavelet transform to get images ensembles. We have also compared our proposed algorithm with some of the efficient methods reported in the literature. Experimental results are given using the IRS images of the Bam city before and after the earthquake.