Segmentation for Image Indexing and Retrieval on Discrete Cosines Domain

This paper used region growing segmentation technique to segment the Discrete Cosines (DC) image. The classic problem of content Based image retrieval (CBIR) is the lack of accuracy in matching between image query and image in the database. By using region growing technique on DC image,it reduced the number of image regions indexed. The proposed of recursive region growing is not new technique but its application on DC images to build  indexing keys is quite new and not yet presented by many  authors. The experimental results show that the proposed methods on segmented images present good precision which are higher than 0.60 on all classes. So, it could be concluded that region growing segmented based CBIR more efficient   compared to DC images in term of their precision 0.59 and 0.75, respectively. Moreover, DC based CBIR can save time and simplify algorithm compared to DCT images. The most significant finding from this work is instead of using 64 DCT coefficients this research only used 1/64 coefficients which is DC coefficient.

KEYWORD AND IMAGE CONTENT FEATURES FOR IMAGE INDEXING AND RETRIEVAL WITHIN COMPRESSED DOMAIN

The central problem of most Content Based Image Retrieval approaches is poor quality in terms of sensitivity (recall) and specificity (precision). To overcome this problem, the semantic gap between high-level concepts and low-level features has been acknowledged. In this paper we introduce an approach to reduce the impact of the semantic gap by integrating high-level (semantic) and low-level features to improve the quality of Image Retrieval queries. Our experiments have been carried out by applying two hierarchical procedures. The first approach is called keyword-content, and the second content-keyword. Our proposed approaches show better results compared to a single method (keyword or content based) in term of recall and precision. The average precision has increased by up to 50%

Keyword and Image Content Features for Image Indexing and Retrieval Within Compressed Domain

The central problem of most Content Based Image Retrieval approaches is poor quality in terms of sensitivity (recall) and specificity (precision). To overcome this problem, the semantic gap between high-level concepts and low-level features has been acknowledged. In this paper we introduce an approach to reduce the impact of the semantic gap by integrating high-level (semantic) and low-level features to improve the quality of Image Retrieval queries. Our experiments have been carried out by applying two hierarchical procedures. The first approach is called keyword-content, and the second content-keyword. Our proposed approaches show better results compared to a single method (keyword or content based) in term of recall and precision. The average precision has increased by up to 50%

The Effective of Image Retrieval in Jpeg Compressed Domain

We propose a new method of feature extraction in orderto improve the effective of image retrieving by using apartial Joint Photographic Experts Group (JPEG)compressed images algorithm. Prior to that, we prune theimages database by pre-query step based on coloursimilarity, in order to eliminate image candidates. Ourfeature extraction can be carried out directly to JPEGcompressed images. We extract two features of DCTcoefficients, DC feature and AC feature, from a JPEGcompressed image. Then we compute the Euclideandistances between the query image and the images in adatabase in terms of these two features. The image querysystem will give each retrieved image a rank to define itssimilarity to the query image. Moreover, instead of fullydecompressing JPEG images, our system only needs to dopartial entropy decoding. Therefore, our proposed schemecan accelerate the effectiveness of retrieving images.According to our experimental results, our system is notonly highly effective but is also capable of performingsatisfactoril

Pemanfaatan Aplikasi Sistem Pakar Untuk Diagnosa Penyakit Paru-paru

Expert system to diagnose lung disease was designed as a tool to diagnose the type of lung disease in particular. The knowledge obtained from various sources such as research and exposure to experts in the field and conducted a book-related lung disease. Inference in an expert system which is made using the method of Forward Chaining. Expert system will display a selection of symptoms that can be selected by the user to get the final result. At the end of the expert system will display the type of lung disease, information about lung disease and herbal medicine for lung disease

Model of the Empowerment of Governance Based on the Human Resource Management for Supply Chains in Higher Education

The change management in higher education governance is able to adapt into a global change of businesses and sustainable technologies based on supply chain strategy. This study aims to assess the empowerment profile-based on the change management governance quality by human resource management. This research uses quantitative method to collecting data and documentation by triangulation technique to determine the main factors of quality policy, mechanism of reference preparation theory to fit theoretical reference and quality guarantee. The results show that there are changes of quality based on the governance management because of the main factor or the quality target indicators. The next result is the leadership variable which is main factor in accountable management, which of the good indicators to assess the overall management of universities proven indirectly to have is one a positive impact on changes in the performance of higher education management. This shows evidence that universities must consider the implementation of visionary leadership factors to improve the performance of human resource management in universities

KEYWORD AND IMAGE CONTENT FEATURES FOR IMAGE INDEXING AND RETRIEVAL WITHIN COMPRESSED DOMAIN

The central problem of most Content Based Image Retrieval approaches is poor quality in terms of sensitivity (recall) and specificity (precision). To overcome this problem, the semantic gap between high-level concepts and low-level features has been acknowledged. In this paper we introduce an approach to reduce the impact of the semantic gap by integrating high-level (semantic) and low-level features to improve the quality of Image Retrieval queries. Our experiments have been carried out by applying two hierarchical procedures. The first approach is called keyword-content, and the second content-keyword. Our proposed approaches show better results compared to a single method (keyword or content based) in term of recall and precision. The average precision has increased by up to 50%