The recent advancement in the research fields of medical imaging and machine learning technologies greatly im- prove the accuracy of the content-based medical image retrie- val(Cbmir). Existing Cbmirmethods, however, often overlooks the effect of image details on retrieval accuracy, which is usually unsatisfactory. The Cbmirof high-definition CT image (CI) based on deep learning technology, combined with patient symptom information, can precisely assist physicians in disease diagnosis and treatment. To address this issue, we take liver CI as an example in this article, and propose a privacy-preservingComputer-AidedDiagnosis(Cad) method of the liver diseases based on the multi-modal clinical information (e.g., patients’ CIs and symptoms, etc). Four enabling techniques such as aWsslnmodel, a unified similarity measure of CIs, a DM-Treeindex and a lightweight privacy-preserving(LPP) scheme are pre- sented to facilitate theCadprocessing of the large liver CIs. Ex- tensive experiments demonstrate that our proposedCadme- thod outperform the state-of-the-art schemes by a large margin in terms of the retrieval accuracy and efficiency, respectively.

This article addresses a multi-query optimization problem for distributed medical image retrieval in mobile wireless networks by exploiting the dependencies in the derivation of a retrieval evaluation plan. To the best of our knowledge, this is the first work investigating batch medical image retrieval (BMIR) processing in a mobile wireless network environment. Four steps are incorporated in our BMIR algorithm. First, when a number of retrieval requests (i.e.,mretrieval images andmradii) are simultaneously submitted by users, then a cost-based dynamic retrieval (CDRS) scheduling procedure is invoked to efficiently and effectively identify the correlation among the retrieval spheres (requests) based on a cost model. Next, an index-based image set reduction (ISR) is performed at the execution-node level in parallel. Then, a refinement processing of the candidate images is conducted to get the answers. Finally, at the transmission-node level, the corresponding image fragment (IF) replicas are chosen based on an adaptive multi-resolution (AMR)-based IF replicas selection scheme, and transmitted to the user-node level by a priority-based IF replicas transmission (PIFT) scheme. The experimental results validate the efficiency and effectiveness of the algorithm in minimizing the response time and increasing the parallelism of I/O and CPU.

Ancient language manuscripts constitute a key part of the cultural heritage of mankind. As one of the most important languages, Chinese historical calligraphy work has contributed to not only the Chinese cultural heritage but also the world civilization at large, especially for Asia. To support deeper and more convenient appreciation of Chinese calligraphy works, based on our previous work on the probabilistic retrieval of historical Chinese calligraphic character manuscripts repositories, we propose a system framework of the multi-feature-based Chinese calligraphic character images probabilistic retrieval in the mobile cloud network environment, which is called the DPRC . To ensure retrieval efficiency, we further propose four enabling techniques: (1) DRL-based probability propagation, (2) optimal data placement scheme, (3) adaptive data robust transmission algorithm, and (4) index support filtering scheme. Comprehensive experiments are conducted to testify the effectiveness and efficiency of our proposed DPRC method.

This paper proposes a partition-based uncertain high-dimensional indexing algorithm, called PU-Tree. In the PU-Tree, all (n)data objects are first grouped into some clusters by a k-Means clustering algorithm. Then each object's corresponding uncertain sphere is partitioned into several slices in terms of the zero-distance. Finally a unified key of each data object is computed by adopting multi-attribute encoding scheme, which are inserted by a B+-tree. Thus, given a query object, its probabilistic range search in high-dimensional spaces is transformed into the search in the single dimensional space with the aid of the PU-Tree. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of the proposed scheme.

The research of cognitive science indicates that manifold-learning-based facial image retrieval is based on human perception, which can accurately capture the intrinsic similarity of two facial images. The paper proposes a pivot-based Distributed Pseudo Similarity Retrieval method called DPSR in manifold spaces with the aid of a adjacency distance list (ADL). Specifically, we first construct a two dimensional array, called ADL which records the pair-wise distance between any two facial images with a constraint in the database. Then, the distances are indexed by a B+-tree. Finally, a DPSR process in high-dimensional manifold spaces is transformed into range search over the B+-tree in the single-dimensional space at a filtering level. Extensive experimental studies show that the DPSR outperforms the conventional sequential scan in manifold spaces by a large margin, especially for the large high-dimensional datasets.

A well arranged task priority in dual-shore collaborative software design phase will smooth the collaboration and reduces the potential coordination lag. However, it is really difficult to determine the priority when there are many collaboration tasks and the collaborative process is a little complicated. On the basis of behavior compatibility analysis using Petri-net, this paper proposed yet a new automatic method to determine the priority. The collaborative design is modeled in Petri-net and some algorithms are presented for the automatic priority determining.

Due to the well-known dimensionality curse problem, search in a high-dimensional space is considered as a "hard" problem. In this paper, a novel symmetrical encoding-based index structure, which is called EHD-Tree (for symmetrical Encoding-based Hybrid Distance Tree), is proposed to support fast k-Nearest-Neighbor (k-NN) search in high-dimensional spaces. In an EHD-Tree, all data points are first grouped into clusters by a k-Means clustering algorithm. Then the uniform ID number of each data point is obtained by a dual-distance-driven encoding scheme in which each cluster sphere is partitioned twice according to the dual distances of start- and centroid-distance. Finally, the uniform ID number and the centroid-distance of each data point are combined to get a uniform index key, the latter is then indexed through a partition-based B+-tree. Thus, given a query point, its k-NN search in high-dimensional spaces can be transformed into search in a single dimensional space with the aid of the EHD-Tree index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of our proposed scheme, and the results demonstrate that this method outperforms the state-of-the-art high dimensional search techniques such as the X-Tree, VA-file, iDistance and NB-Tree, especially when the query radius is not very large.

This paper considers a multi-query optimization issue for distributed similarity query processing, which attempts to exploit the dependencies in the derivation of a query evaluation plan. To the best of our knowledge, this is the first work investigating a multi- query optimization technique for distributed similarity query processing (MDSQ). Four steps are incorporated in our MDSQ algorithm. First when a number of query requests(i.e., m query vectors and m radiuses) are simultaneously submitted by users, then a cost-based dynamic query scheduling(DQS) procedure is invoked to quickly and effectively identify the correlation among the query spheres (requests). After that, an index-based vector set reduction is performed at data node level in parallel. Finally, a refinement process of the candidate vectors is conducted to get the answer set. The proposed method includes a cost-based dynamic query scheduling, a Start-Distance(SD)-based load balancing scheme, and an index-based vector set reduction algorithm. The experimental results validate the efficiency and effectiveness of the algorithm in minimizing the response time and increasing the parallelism of I/O and CPU.

The large numbers of Chinese calligraphic scripts in existence are valuable part of the Chinese cultural heritage. However, due to the shape complexity of these characters, it is hard to employ existing techniques to effectively retrieve and efficiently index them. In this article, using a novel shape-similarity- based retrieval method in which shapes of calligraphic characters are represented by their contour points extracted from the character images, we propose an interactive partial-distance-map (PDM)- based high-dimensional indexing scheme which is designed specifically to speed up the retrieval performance of the large Chinese calligraphic character databases effectively. Specifically, we use the approximate minimal bounding sphere of a query character and utilize users' relevance feedback to refine the query gradually. Comprehensive experiments are conducted to testify the efficiency and effectiveness of this method. In addition, a new k -NN search called Pseudo k -NN (P k -NN) search is presented to better facilitate the PDM-based character retrieval.

In this paper, based on a novel shape-similarity-based retrieval method, we propose an interactive partial-distance-map (PDM)- based high-dimensional indexing scheme to speed up the retrieval performance of the large Chinese calligraphic character databases. Specifically, we use the approximate minimal bounding hyper- sphere of query character to search the PDM and utilize the users' relevance feedback to refine the search process. We conduct comprehensive experiments to testify the efficiency and effectiveness of the proposed method.