A Novel Multiscale Fourier Descriptor Based on Plane Orthogonal Vectors for Fast Shape Retrieval Shape retrieval is an interesting and challenging issue in computer vision and robot vision. The recent works employing Fourierbased multiscale descriptors achieve some success in shape retrieval, especially make realtime application possible by improving the efficiency. However, these methods are commonly adopting really complex shape descriptors to present the shape information which is not intuitive enough. In our work, we propose a new Fourierbased method with novel multiscale descriptor which is a combination of contour feature and regional feature. This novel descriptor has two main characteristics: First, utilize six different invariant signatures which are based on plane orthogonal vectors; Second, leverage multiple scale structures to describe local and global information of shapes. Except the easiness in understanding, this descriptor approach also produces a more distinct topology of shapes than other approaches which means the property of stability on variations, linear or nonlinear. The method is tested on some popular databases that provides a promising retrieval rate(87.46% and 99.27%) with constant time complexity optimization. The results confirm that our method is more capable of representing the topology of shapes than most other methods based on Fourier descriptor.

A Color Image Representation Approach for ContentBased Image Retrieval In this paper, a color image representation approach is proposed for color image retrieval. First, an input color image is represented by a number representative images (called color degree images) consisting of chromatic (including red, green, blue, cyan, magenta, and yellow) and achromatic (including black and white) degree images. For each pixel, the degree to which its color value is similar to each representative color will be computed. For the local window centered at each detected key point, a vector representing the degree values, called local color degree (LCD) descriptor, will be computed to describe the local color property. Meanwhile, the scaleinvariant feature transform (SIFT) descriptor is extracted to describe the local texture characteristics. Both LCD descriptor and SIFT descriptor are combined together to form the local descriptor. Vector of locally aggregated descriptors (VLAD) is then employed to aggregate all local descriptors to get a single VLAD vector for each input image. Experimental results on INRIA Holidays and Leeds Butterfly datasets have shown that the proposed descriptor yield higher retrieval accuracy.