
Although humans perceive the luster of objects visually and sensuously on a daily basis, there is much debate as to how to express this numerically in order to obtain an index of luster. Gloss can be intentionally expressed by painting something on the 3DCG object. Therefore, we thought that we could somehow quantify the glossiness of 3D CG objects by studying the relationship between glossiness and paint. In this paper, we set parameters related to glossiness and paint on 3D CG object images in the Shitsukan Perception Standard Problem image dataset, performed texture analysis on these patterns, and discussed the results by classifying evaluation values using a Support Vector Machine (SVM) in relation to glossiness, paint, and image quality.

To improve the efficiency and accuracy of Content Based Image Retrieval (CBIR) for specific images, a new method is presented in the paper. The method focuses on 3 key problems. Firstly, considering the impaction of saliency point near the attention focus, an improved saliency region extraction algorithm is proposed to locate object of interest more accurately. Then, the construction of Bag-of-Features (BoF) feature vector is improved by our visual attention model to extract features more effectively. Finally, Particle Swarm Optimization (PSO) is introduced to optimize the learning process of the feedback model based on Support Vector Machine (SVM) to boost the accuracy and efficiency of the image retrieval. Experiments and comparison between typical algorithms based on Caltech 101 dataset and self-collection dataset demonstrate that the method proposed in this paper can improve the accuracy and efficiency of content based image retrieval.