In existing image retrieval systems using a multipoint query, the neighborhood of the various optimal query points is determined in the same way. The different nature of the various optimal query points was not taken into consideration. This approach confines the performance of the system. To overcome this confinement, in this paper, we propose an image retrieval method through adaptive weights (Aweight) which is possible to compute the various optimal query points, optimal weights and improved distance functions to improve accuracy. In addition, our method constructs clusters without re-clustering the whole feedback image set. The experiments were performed on a set of 10,800 images and the results demonstrate that the proposed method improves performance of system in terms of accuracy.

Many previous techniques were designed to retrieve semantic images in a particular neighborhood of the image query and thus bypassing the semantically related images in the whole feature space. Several methods were originally designed to retrieve semantically images in the same space feature but with low precision. In this paper, we propose a semantic - Related Image Retrieval method (SRIR), which can retrieve semantic images of the same space in high precision. Our method takes advantage of the user feedback to determine the semantic importance of each query and the importance of each feature. In addition, the retrieval time of our method does not increase with the number of user feedback. We also provide experimental results to demonstrate the effectiveness of our method.

Quality, efficiency and scalability are the key issues in the design of image retrieval systems for large image databases. Although the quality of image retrieval methods still depends strongly on the application domain, color based retrieval techniques have been shown to be competitive and generally applicable. However, the retrieval algorithms based on color histograms largely ignore spatial information in the matching process. In this paper, we propose a technique to improve the retrieval process by image regions matching. We carried out an experiment on an image database containing 8000 images. The experimental results show that our proposed technique is more effective than the other retrieval techniques such as color histogram based and Color Based Clustering based techniques.

Dao Nam Anh, Pham Quang Huy, Luong Chi Mai, Watermark by Learning Non Saliency, FICTA 2018, will be published in Springer, Advances in Intelligent Systems and Computing (AISC) series. Indexed by ISI Proceedings, Dblp, Ulrich's, Scopus, Springerlink, ppsx.

This paper studies the capability of non-salient image regions and pattern selection for advancing the security robustness of region based watermark. However, considerable unpredictability in the assigning image region for encryption exists, since saliency map itself is changed specifically by particular encryption algorithm and saliency detection techniques. The change may lead to misguided selection of image region for decryption. A new solution for watermarking in non-salient region is described and evaluated to derive improvement of secrecy, integrity and availability of images. This is achieved by using machine learning approach, which favors the advantageous selection of encryption region based on the variation between the salient maps before and after the encryption process. We validate the method by region selection evaluation and the correctness of decrypted messages. Experimental results show that a range of saliency models are adequate for the proposed watermark solution.

 
Dao Nam Anh, Sparse Nonlocal Texture Mean for Allocation of Irregularity in Images of Brain, Information Systems Design and Intelligent Applications, Advances in Intelligent Systems and Computing 672, Springer, Singapore, ISSN 2194-5357,  pp 618-628, 2018. ISSN 2194-5365 (electronic), ISBN 978-981-10-7511-7 ISBN 978-981-10-7512-4 (eBook), doi: 978-981-10-7512-4_61, old name: Advances in Intelligent and Soft Computing,  Scopus index, impact factor, pptx.

The medical image analysis for irregularity studies has always been a refreshing research topic for the need of efficient and precise diagnosis. A new method based on patch analysis for detection of disorder in images of brain is introduced with machine learning techniques. In the method a sparse nonlocal texture mean filter is proposed to evaluate the similarity of each spot in the image. The spot-based similarity allows initial identification of place of abnormality which is then refined by the support vector machines to efficiently perform extraction of disorder’s region. Experimental results on a benchmark’s real data are assessed and compared objectively to ensure sufficient certainty of the method.