Content Based Image Retrieval, CBIR, performed an automated classification task for a queried image. It could relieve a user from the laborious and timeconsuming metadata assigning for an image while working on massive image collection. For an image, user's definition or description is subjective where it could belong to different categories as defined by different users. Human based categorization and computer-based categorization might produce different results due to different categorization criteria that rely on dataset structure and the clustering techniques. This paper is aimed to exhibit an idea for planning the dataset structure and choosing the clustering algorithm for CBIR implementation. There are 5 sections arranged in this paper; CBIR and QBE concepts are introduced in Section 1, related image categorization research is listed in Section 2, the 5 type of image clustering are described in Section 3, comparative analysis in Section 4, and Section 5 conclude this study. Outcome of this paper will be benefiting CBIR developer for various applications.
Considering the increasing of the number cars on the roads, the rate of road accident has also increased with many people died or sustained serious injuries in a road accident. Increasing the phenomenon of road accident frequency, a study on the factors that may be associated with the occurrence of the accident was conducted. This paper also discussed about r accident prevention method based on the factors studied. The study of this paper can provide forceful data analysis support for the road traffic safety related research.
A care of recurrent Askin`s tumor is presented occurring in a 13 year old boy. Askin’s tumor is a rare primitive neuroectodermal tumor of chest wall and belong to Ewing family of soft tissue sarcoma. It is extremely malignant with a high frequency of both metastatic spread and local recurrence. Multi-drug chemotherapy as well as local disease control with surgery and / or radiation is indicated in the treatment of all patients
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.