Image Retrieval Using Content Information

Abstract: With the rapid development of the multimedia technologies such as compression and digital image sensor technologies, it is more and more necessary to build up a large database. Therefore robust and efficient methodology for image retrieval techniques has gained the attention of researchers. In the last few years the booming interest in the web images has paved a way to make people on content based Image retrieval techniques and also supporting systems for the retrieval process. In this paper, a general review of Content based image retrieval process by comparing with existing retrieval techniques along with experimental results and applications are presented. Some of the major challenges in the adaptation of content based image retrieval algorithms are also discussed

Colour texture classification from colour filter array images using various colour spaces

International audienceThis paper focuses on the classification of colour textures acquired by single-sensor colour cameras. In such cameras, the Colour Filter Array (CFA) makes each photosensor sensitive to only one colour component, and CFA images must be demosaiced to estimate the final colour images. We show that demosaicing is detrimental to the textural information because it affects colour texture descriptors such as Chromatic Co-occurrence Matrices (CCMs). However, it remains desirable to take advantage of the chromatic information for colour texture classification. This information is incompletely defined in CFA images, in which each pixel is associated to one single colour component. It is hence a challenge to extract standard colour texture descriptors from CFA images without demosaicing. We propose to form a pair of quarter-size colour images directly from CFA images without any estimation, then to compute the CCMs of these quarter-size images. This allows us to compare textures by means of their CCM-based similarity in texture classification or retrieval schemes, with still the ability to use different colour spaces. Experimental results achieved on benchmark colour texture databases show the effectiveness of the proposed approach for texture classification, and a complexity study highlights its computational efficiency

Mapping and Localization in Urban Environments Using Cameras

In this work we present a system to fully automatically create a highly accurate visual feature map from image data aquired from within a moving vehicle. Moreover, a system for high precision self localization is presented. Furthermore, we present a method to automatically learn a visual descriptor. The map relative self localization is centimeter accurate and allows autonomous driving