Color Histogram

The present work considers the development of a wheelchair for people with special needs, which is capable of navigating semi-autonomously within its workspace. This system is expected to prove useful to people with impaired mobility and limited fine motor control of the upper extremities. Among the implemented behaviors of this robotic system are the avoidance of obstacles, the motion in the middle of the free space and the following of a moving target specified by the user (eg, a person walking in front of the wheelchair). The ...

The large numbers of images has posed increasing challenges to computer systems to store and manage data effectively and efficiently. This paper implements a CBIR system using different feature of images through four different methods, two were based on analysis of color feature and other two were based on analysis of combined color and texture feature using wavelet coefficients of an image. To extract color feature from an image, one of the standard ways i.e. color histogram was used in YCbCr color space and HSV color space. Daubechies’ wavelet transformation and Symtel’s wavelet transform were performed to extract the texture feature of an image. In this paper a color image retrieval system is illustrated, in which the novelty lies in the use of a fuzzy partition of the HSV color space and wavelet transformation of the fuzzified new image. To increase efficiency of the system finally an image retrieval method was proposed using curvelet transform of an image, which provides an opportunity to extract more accurate texture feature for image retrieval. After obtaining all experimental results, a comparative study was done. From the result it was inferred that curvelet based method gave a better performance as compared to other methods.

Recent image retrieval method focuses on combine low-level features and high-level semantics of image in query processing. This paper introduces a new efficient method of image retrieval system. This method called MMM (Markov model mediator) takes into consideration low-features and high-level semantics of images. Low-features of images are extracted from color histogram and image segmentation. High-level semantics is learned from the history of user’s access pattern and access frequencies on the images. Low-features and high-level semantics of images are combined by Markov model mediator. Note that MMM was proposed by authors Mei-Ling Shyu, Shu-Ching Chen, Na Zhao, Min Chen, Chengcui Zhang, Kanoksri Sarinnapakorn. I only implement MMM as a computer software and test MMM. The computer software is named AGmagic.