We study several problems in image retrieval systems. These problems and pro-posed techniques are divided into three parts. Part I: This part focuses on robust object representation, which is of fundamental importance in computer vision. We target this problem without using specific object mod-els. This allows us to develop methods that can be applied to many different problems. Three approaches are proposed that are insensitive to different kind of object or image changes. First, we propose using the inner-distance, defined as the length of shortest paths within shape boundary, to build articulation insensitive shape descriptors. Second, a deformation insensitive framework for image matching is presented, along with an insensitive descriptor based on geodesic distances on image surfaces. Third, we use a gradient orientation pyramid as a robust face image representation and apply it to the task of face verification across ages. Part II: This part concentrates on comparing histogram-based descriptors that are widely used in image retrieval. We first present an improved algorithm of the Earth Mover’s Distance (EMD), which is a popular dissimilarity measure between histograms. The ne
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