Abstract. A method for exploiting the information in low-level image segmentations for the purpose of object recognition is presented. The key idea is to use a whole ensemble of se...
Computer vision researchers have recently proposed several local descriptor schemes. Due to lack of database support, however, these descriptors have only been evaluated using sma...
Because of the growing use of multimedia content over Internet, Content-Based Image Retrieval (CBIR) has recently received a lot of interest. While accurate search techniques base...
During last years, local image descriptors have received much attention because of their efficiency for several computer vision tasks such as image retrieval, image comparison, fea...
Various local descriptors have been used successfully in a variety of tasks including object recognition. Although different descriptors have been shown to have different strength...
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
Image categorization involves the well known difficulties with different visual appearances of a single object, but introduces also the problem of within-category variation. This ...
Search is not inherent in the correspondence problem. We propose a representation of images, called intrinsic curves, that combines the ideas of associative storage of images with...
Recent work in object categorization often uses local image descriptors such as SIFT to learn and detect object categories. Such descriptors explicitly code local appearance and h...