Cedd: Color and edge directivity descriptor: A compact descriptor for image indexing and retrieval

Abstract. This paper deals with a new low level feature that is extracted from the images and can be used for indexing and retrieval. This feature is called “Color and Edge Directivity Descriptor ” and incorporates color and texture information in a histogram. CEDD size is limited to 54 bytes per image, rendering this descriptor suitable for use in large image databases. One of the most important attribute of the CEDD is the low computational power needed for its extraction, in comparison with the needs of the most MPEG-7 descriptors. The objective measure called ANMRR is used to evaluate the performance of the proposed feature. An online demo that implements the proposed feature in an image retrieval system is available at

Neural Network Schemes in Cartesian Space Control of Robot Manipulators

In this paper we are studying the Cartesian space robot manipulator control problem by using Neural Networks (NN). Although NN compensation for model uncertainties has been traditionally carried out by modifying the joint torque/force of the robot, it is also possible to achieve the same objective by using the NN to modify other quantities of the controller. We present and evaluate four different NN controller designs to achieve disturbance rejection for an uncertain system. The design perspectives are dependent on the compensated position by NN. There are four quantities that can be compensated: torque , force F, control input U and the input trajectory Xd. By defining a unified training signal all NN control schemes have the same goal of minimizing the same objective functions. We compare the four schemes in respect to their control performance and the efficiency of the NN designs, which is demonstrated via simulations

Automatic Image Annotation and Retrieval Using the Joint Composite Descriptor

Abstract—Capable tools are needed in order to successfully search and retrieve a suitable image from large image col-lections. Many content-based image retrieval systems employ low-level image features such as color, texture and shape in order to locate the image. Although the above approaches are successful, they lack the ability to include human perception in the query for retrieval because the query must be an image. In this paper a new image annotation technique and a keyword-based image retrieval system are presented, which map the low-level features of the Joint Composite Descriptor to the high-level features constituted by a set of keywords. One set consists of colors-keywords and the other set consists of words. Experiments were performed to demonstrate the effectiveness of the proposed technique