We propose multi-precision similarity matching where the image is divided into a number of subblocks, each with its associated color histogram. We present experimental results showing that the spatial distribution information recorded by multiprecision color histograms helps to make similarity matching more precise. We also show that sub-image queries are much better supported with multi-precis...

We present a method for image retrieval by color and shape similarity matching using metric indexing. We extract color features into the HSV color and for shape features extraction we use Fourier descriptors. We index the combined color and shape features vectors into a M-tree. We complement the use of the M-tree with a filtering technique that also permits partial-image matching. We use a simi...

Color is one of the most important low-level features used in image analysis and retrieval. In this paper we introduce an algorithm for extracting compact color feature in the CIELAB color space. Our method is based on an ant colony clustering algorithm, which helps us develop a perceptually based image similarity metric. We propose a dominant color and area percentage model, which provides a v...

We propose a new algorithm for color transfer between images that have perceptually similar semantic structures. We aim to achieve a more accurate color transfer that leverages semanticallymeaningful dense correspondence between images. To accomplish this, our algorithm uses neural representations for matching. Additionally, the color transfer should be spatially-variant and globally coherent. ...