On fusing the latent deep CNN feature for image classification
Image classification, which aims at assigning a semantic category to images, has been extensively studied during the past few years. More recently, convolution neural network arises and has achieved very promising achievement. Compared with traditional ...
A resource-aware approach for authenticating privacy preserving GNN queries
Nowadays many location service providers (LSPs) employ spatial databases outsourced from a third-party data owner (DO) to answer various users' queries, e.g., group nearest neighbor (GNN) queries that enable a group of users to find a meeting place ...
Effective shortest travel-time path caching and estimating for location-based services
For location-based services (LBS), the path with the shortest travel time is much more meaningful than the one with the shortest network distance, as it considers the live traffics in road networks. Usually, there are two ways for an LBS provider to ...
Class consistent hashing for fast Web data searching
Hashing based ANN search has drawn lots of attention due to its low storage and time cost. Supervised hashing methods can leverage label information to generate compact and accurate hash codes and have achieved promising results. However, when dealing ...
MARES: multitask learning algorithm for Web-scale real-time event summarization
Automatic real-time summarization of massive document streams on the Web has become an important tool for quickly transforming theoverwhelming documents into a novel, comprehensive and concise overview of an event for users. Significant progresses have ...
Low-rank hypergraph feature selection for multi-output regression
Current multi-output regression method usually ignores the relationship among response variables, and thus it is challenging to obtain an effective coefficient matrix for predicting the response variables with the features. We address these problems by ...
ProfitLeader: identifying leaders in networks with profit capacity
Identifying `Leaders' in a network is a significant step to optimize the use of available resources, accelerate or hinder spreading information. In this paper, we propose a new measure to characterize the importance of a node, called ProfitLeader, which ...
Multi-scale deep context convolutional neural networks for semantic segmentation
Recent years have witnessed the great progress for semantic segmentation using deep convolutional neural networks (DCNNs). This paper presents a novel fully convolutional network for semantic segmentation using multi-scale contextual convolutional ...
Deep learning approaches for video-based anomalous activity detection
The pervasive use of cameras at indoor and outdoor premises on account of recording the activities has resulted into deluge of long video data. Such surveillance videos are characterized by single or multiple entities (persons, objects) performing ...
A novel approach for Web page modeling in personal information extraction
The target of personal information extraction (PIE) is to extract content associated with a name form Web pages. Available Web page models, which are also used widely in content extraction and automatic wrapper algorithms, include text model, document ...
Residual attention-based LSTM for video captioning
Recently great success has been achieved by proposing a framework with hierarchical LSTMs in video captioning, such as stacked LSTM networks. When deeper LSTM layers are able to start converging, a degradation problem has been exposed. With the number ...
Predicting e-book ranking based on the implicit user feedback
In this paper, we plan to predict a ranking on e-books by analyzing the implicit user behavior, and the goal of our work is to optimize the ranking results to be close to that of the ground truth ranking where e-books are ordered by their corresponding ...
Deep adversarial metric learning for cross-modal retrieval
Cross-modal retrieval has become a highlighted research topic, to provide flexible retrieval experience across multimedia data such as image, video, text and audio. The core of existing cross-modal retrieval approaches is to narrow down the gap between ...
Group sparse reduced rank regression for neuroimaging genetic study
The neuroimaging genetic study usually needs to deal with high dimensionality of both brain imaging data and genetic data, so that often resulting in the issue of curse of dimensionality. In this paper, we propose a group sparse reduced rank regression ...
Abnormal event detection in tourism video based on salient spatio-temporal features and sparse combination learning
With the booming development of tourism, travel security problems are becoming more and more prominent. Congestion, stampedes, fights and other tourism emergency events occurred frequently, which should be a wake-up call for tourism security. Therefore, ...
Co-regularized kernel ensemble regression
In this paper, co-regularized kernel ensemble regression scheme is brought forward. In the scheme, multiple kernel regressors are absorbed into a unified ensemble regression framework simultaneously and co-regularized by minimizing total loss of ...
Exploiting long-term temporal dynamics for video captioning
Automatically describing videos with natural language is a fundamental challenge for computer vision and natural language processing. Recently, progress in this problem has been achieved through two steps: 1) employing 2-D and/or 3-D Convolutional ...
Context-aware graph pattern based top-k designated nodes finding in social graphs
Graph Pattern Matching (GPM) plays a significant role in many real applications, where many applications often need to find Top-K matches of a specific node, (named as the designated node vd) based on a pattern graph, rather than the entire set of ...
Global-view hashing: harnessing global relations in near-duplicate video retrieval
Multi-view features are often used in video hashing for near-duplicate video retrieval because of their mutual assistance and complementarity. However, most methods only consider the local available information in multiple features, such as individual ...
Dilated-aware discriminative correlation filter for visual tracking
Recent progress has witnessed continued attention in discriminative correlation filter (DCF) tracking algorithms due to its high-efficiency. However, the existing DCF inevitably introduces some cyclic repetitions in learning and detection, which might ...
Generalized zero-shot learning for action recognition with web-scale video data
Action recognition in surveillance video makes our life safer by detecting the criminal events or predicting violent emergencies. However, efficient action recognition is not free of difficulty. First, there are so many action classes in daily life that ...
Semi-supervised cross-modal learning for cross modal retrieval and image annotation
Multimedia data are usually associated with multiple modalities represented by heterogeneous features. Recently, many information retrieval tasks are not only restricted to the case of a single modal and the contend-based cross modal retrieval has ...
An emotion-based responding model for natural language conversation
As an important task of artificial intelligence, natural language conversation has attracted wide attention of researchers in natural language processing. Existing works in this field mainly focus on consistency of neural response generation whilst ...
Protecting multi-party privacy in location-aware social point-of-interest recommendation
Point-of-interest (POI) recommendation has attracted much interest recently because of its significant business potential. Data used in POI recommendation (e.g., user-location check-in matrix) are much more sparse than that used in traditional item (...
Practical k-agents search algorithm towards information retrieval in complex networks
Spanking information retrieval in large-scale Web and network has attracted increasing interest in the research community, many typical approaches have been recalled such as greedy, random-walk and high degree seeking since the search capabilities of ...
Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification
In this paper, we propose a novel dimensionality reduction method of taking the advantages of the variability, sparsity, and low-rankness of neuroimaging data for Alzheimer's Disease (AD) classification. We first take the variability of neuroimaging ...