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Chapter and Conference Paper
MVCAL: Multi View Clustering for Active Learning
Various active learning methods with ingenious sampling strategies have been proposed to solve the lack of labeled samples in supervised learning, but most are designed for specific tasks. In this paper, we pr...
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Chapter and Conference Paper
A Novel Attention Enhanced Dense Network for Image Super-Resolution
Deep convolutional neural networks (CNNs) have recently achieved impressive performance in image super-resolution (SR). However, they usually treat the spatial features and channel-wise features indiscriminati...
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Chapter and Conference Paper
Web Service Composition by Optimizing Composition-Segment Candidates
Web service composition has been increasingly challenging in recent years due to the escalating number of services and the diversity of task objectives. Despite many researches have already addressed the optim...
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Chapter and Conference Paper
SCOD: Dynamical Spatial Constraints for Object Detection
One-stage detectors are widely used in real-world computer vision applications nowadays due to their competitive accuracy and very fast speed. However, for high resolution (e.g.,
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Chapter and Conference Paper
Two-Stage Unsupervised Deep Hashing for Image Retrieval
In this paper, we propose a two-stage unsupervised deep hashing method to map the high-dimensional images to compact binary codes for large-scale image retrieval. We employ a neural network to look for the non...
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Chapter and Conference Paper
Single Image Super-Resolution via Perceptual Loss Guided by Denoising Auto-Encoder
Image restoration is a difficult task due to its non-uniqueness of solution. Owing to the power of Convolution Neural Networks (CNNs), we can generate images with high PSNR (Peak Signal to Noise Ratio) and SSI...
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Chapter and Conference Paper
Matching Attention Network for Domain Adaptation Optimized by Joint GANs and KL-MMD
Although deep neural networks have brought impressive advances in a variety of machine learning tasks, it is more difficult to train a top-performing model in the absence of the labeled data. To alleviate this...
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Chapter and Conference Paper
Supervised Web Service Composition Integrating Multi-objective QoS Optimization and Service Quantity Minimization
The QoS of web service has been increasingly crucial due to the escalating number of services with similar or identical functionality, which leads to intensive researches on QoS-aware web service composition. ...
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Chapter and Conference Paper
Efficient Web Service Composition via Knapsack-Variant Algorithm
Since the birth of web service composition, the minimization of the number of web services in the resulting composition while satisfying user requests has been a significant perspective of research. With the i...
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Chapter and Conference Paper
Large-Scale QoS-Aware Service Composition Integrating Chained Dynamic Programming and Hybrid Pruning
Providing both optimal QoS and a minimum number of services simultaneously is a promising perspective of QoS-aware service composition, whereas most existing research studies are still unfavorable toward makin...
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Article
Spatial locality-preserving feature coding for image classification
The state-of-the-art image classification models, generally including feature coding and pooling, have been widely adopted to generate discriminative and robust image representations. However, the coding schem...
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Chapter and Conference Paper
Binary Code Learning via Iterative Distance Adjustment
Binary code learning techniques have recently been actively studied for hashing based nearest neighbor search in computer vision applications due to its merit of improving hashing performance. Currently, hashi...
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Chapter and Conference Paper
Effective Citation Recommendation by Unbiased Reference Priority Recognition
Citation recommendation is a meaningful and challenging research problem nowadays. Most of prior researches make a simplified assumption that the citations are more preferential for the papers to cite than the...
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Chapter and Conference Paper
MapReduce-based Parallelized Approximation of Frequent Itemsets Mining in Uncertain Data
In recent years, frequent itemsets mining in uncertain data has drawn increasingly attractions from data mining communities. Currently, frequent itemsets mining algorithms in uncertain data mainly use frequent...
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Article
Image annotation by modeling Supporting Region Graph
Annotating image regions with keywords has received increasing attention in the computer vision community in recent years. Recent studies have shown that graphical modeling techniques, such as Conditional Rand...
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Chapter and Conference Paper
Efficient Probabilistic Frequent Itemset Mining in Big Sparse Uncertain Data
Probabilistic frequent itemset (PFI) mining in uncertain data has been drawing increasing attention from data mining communities recently. However, data generated in network environments, such as machine logs ...
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Article
Image retrieval based on augmented relational graph representation
The “semantic gap” problem is one of the main difficulties in image retrieval tasks. Semi-supervised learning, typically integrated with the relevance feedback techniques, is an effective method to narrow down...
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Chapter and Conference Paper
Feedback-Based Image Retrieval Using Probabilistic Hypergraph Ranking Augmented by Ant Colony Algorithm
One fundamental issue in image retrieval is its lack of ability to take advantage of relationships among images and relevance feedback information. In this paper, we propose a novel feedback-based image retrie...
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Chapter and Conference Paper
Codebook Quantization for Image Classification Using Incremental Neural Learning and Subgraph Extraction
This paper proposes a fast, incremental codebook quantization algorithm for image classification consisting of a fast codebook graph learning algorithm using incremental neural learning, and a subgraph-based c...
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Chapter and Conference Paper
Image Region Segmentation Based on Color Coherence Quantization
This paper presents a novel approach for image region segmentation based on color coherence quantization. Firstly, we conduct an unequal color quantization in the HSI color space to generate representative col...
