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Multi-label image classification is a fundamental yet challenging task in computer vision that aims to identify multiple objects from a given image. Recent studies on this task mainly focus on learning cross-modal interactions between label semantics and ...

Nowadays, three challenges should be considered in order to build a strong model that is used to extract and semantically interpret the relationship between objects in images namely: long-tail problem, large intra-class divergence, and the semantic ...

Cross-domain Facial Expression Recognition (FER) is challenging due to the difficulty of concurrently handling the domain shift and semantic gap during domain adaptation. Existing methods mainly focus on reducing the domain discrepancy for transferable ...

In this research, a novel integrated semantic interactive algorithm for image retrieval is proposed to retrieve set of relevant images for a given query. The main challenge in image retrieval is to retrieve relevant images with high precision and ...

Virtual machine introspection (VMI) is a technique for the external monitoring of virtual machines. Through previous work, it became apparent that VMI can contribute to the security of distributed systems and cloud architectures by facilitating stealthy ...

The semantic mismatch between query and document terms—i.e., the semantic gap—is a long-standing problem in Information Retrieval (IR). Two main linguistic features related to the semantic gap that can be exploited to improve retrieval are synonymy and ...

The classification-based relevance feedback approach suffers from the problem of imbalanced training dataset, which causes instability and degradation in the retrieval results. In order to tackle with this problem, a novel active learning approach based ...

Two modalities are often used to convey information in a complementary and beneficial manner, e.g., in online news, videos, educational resources, or scientific publications. The automatic understanding of semantic correlations between text and ...

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual ...

Efficient image retrieval from a large image repository is still a challenging task because of the semantic gap. In this paper, a stride is made towards reducing the semantic gap by proposing an efficient approach using relevance feedback and random ...

The contextual information in the built environment is highly heterogeneous, it goes from static information (e.g., information about the building structure) to dynamic information (e.g., user’s space–time information, sensors detections and events that ...

In the last years, there has been much attention given to the semantic gap problem in multimedia retrieval systems. Much effort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from ...

The performance of the semantic concept detection method depends on, the selection of the low-level visual features used to represent key-frames of a shot and the selection of the feature-fusion method. This paper proposes a set of low-level visual ...

This paper presents an innovative concept for a distributed system that combines a smart camera network with semantic reasoning. The proposed system is context sensitive and combines the information extracted by the smart camera with logic rules and ...

Content-based image retrieval CBIR research is currently faced with the so called the 'semantic gap' problem. CBIR researchers work at the near end of the gap, applying computer science methods to bridge the gap. Cognitive psychology researchers work at ...

The dynamics of urban activities in Jerusalem were studied by analyzing a large-scale semantically rich movement dataset. The semantic enrichment process was based on coupling movement trajectories sampled by GPS loggers with contextual data derived ...

Due to heavy clutters and occlusions of complex background, natural images contain complex features in data structure which often cause errors in image classification. In this paper, we propose semi-supervised bi-dictionary learning for image ...

The BoW model is a mainstream in image retrieval, and is employed in many state-of-the-art retrieval process. However, there are two main problems in this model: semantic gap caused by image quantification, and insufficient feature discriminative power. ...

When designing computer monitoring systems, one goal has always been to have a complete view of the monitored target and at the same time stealthily protect the monitor itself. One way to achieve this is to use hypervisor-based, or more generally out of ...

Due to the semantic gap between low-level visual features and high-level semantic content of images, the methods for image annotation based on low-level visual features, cannot well meet the requirement of knowledge discovery from web images. Therefore, ...