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Guest editorial: Bridging the semantic gap in multimedia understanding
Neurocomputing 208 (2016) 1–2
Contents lists available at ScienceDirect
Neurocomputing journal homepage: www.elsevier.com/locate/neucom
Guest editorial: Bridging the semantic gap in multimedia understanding
1. Guest editorial for the issue The explosive growth of visual and textual data, both on the Web and stored in private repositories, has led to urgent requirements in terms of search, processing and management of digital content. Developing optimal solutions to allow access to and mining such data is crucial. For image and video understanding, due to the unconstrained nature of images and videos, and the lack of fully reliable low-level features, the process of image and video understanding can be helped by grounding it with a prior semantic model describing any domain knowledge, which may operate during both learning and inference. An important step for Artificial Intelligence is to bridge the semantic gap between vision and language. This special issue serves as a forum for researchers all over the world to discuss their works and recent advances in learning with semantic information methods and its applications in multimedia analysis. This issue consists of 38 papers, which are briefly discussed as follows. First of all, to provide readers of the special issue with a stateof-the-art background on the topic of learning with semantic information, one survey paper “On interactive learning-to-rank for IR: Overview, recent advances, challenges, and directions” is included in this special issue. There are two papers addressed the topic of ontology design and semantic concept detection in this issue, i.e., (1) “Contextaware ontologies generation with basic level concepts from collaborative tags”, (2) “Multi-source alert data understanding for security semantic discovery based on rough set theory”. Image and video search and retrieval is very useful in our daily life now. This issue consists of five papers about this topic, i.e., (1) “Hierarchical learning of large-margin metrics for large-scale image classification”, (2) “Binary code learning via optimal class representations”, (3) “A correlation graph approach for unsupervised manifold learning in image retrieval tasks”, (4) “An image classification method that considers privacy-preservation”, (5) “A novel dynamic multi-model relevance feedback procedure for contentbased image retrieval”. Learning with semantic information with deep learning framework is a hot topic these days. This issue consists of two papers
http://dx.doi.org/10.1016/j.neucom.2016.05.051 0925-2312/& 2016 Elsevier B.V. All rights reserved.
about this topic, i.e., (1) “People counting based on head detection combining Adaboost and CNN in crowded surveillance environment”, (2) “Research on the natural image super-resolution reconstruction algorithm based on compressive perception theory and deep learning model”. Novel machine learning approach for vision-language integration is important for bridging the semantic gap in multimedia understanding. This issue consists of eleven papers about this topic, i.e., (1) “A classification model for semantic entailment recognition with feature combination”, (2) “Multi-view semisupervised learning for image classification”, (3) “Semi-supervised subspace learning with L2graph”, (4) “On the distance metric learning between cross-domain gaits”, (5) “Transfer subspace learning for cross-dataset facial expression recognition”, (6) “A Laplacian structured representation model in subspace clustering for enhanced motion segmentation”, (7) “Discriminative sparse projections for activity-based Person recognition”, (8) “A framework of uniform contribution embedding of data”, (9) “Biased subspace learning for misalignment-robust facial expression recognition”, (10) “Clustering by fast search and find of density peaks via heat diffusion”, (11) “Hybrid generative-discriminative learning for online tracking of sperm cell”. Moreover, there are many interesting real-world multimedia applications based on learning with semantic information. This issue consists of seventeen papers about this topic, i.e., (1) “Breaking down violence detection: Combining divide-et-impera and coarseto-fine strategies”, (2) “Sky detection by effective context inference”, (3) “Age progression: Current technologies and applications”, (4) “Joint local regressors learning for face alignment”, (5) “Effect of variation in gesticulation pattern in dynamic hand gesture recognition system”, (6) “Local surface geometric feature for 3D human action”, (7) “Evaluation of Kinect2 based balance measurement”, (8) “Spatial and temporal scoring for egocentric video summarization”, (9) “Robust plant cell tracking using local spatial-temporal context”, (10) “Incremental image set querying based localization”, (11) “Reduce false positives for object detection by a priori probability in videos”, (12) “Bridge the semantic gap between pop music feature and emotion: Build an interpretable model”, (13) “Visual homograph: A novel basic visual element”, (14) “Building change detection with RGB-D map generated from UAV images”, (15) “Multi-
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Y. Yan and J. Lu / Neurocomputing 208 (2016) 1–2
target tracking via hierarchical association learning”, (16) “Exponential discriminant locality preserving projection for face recognition, (17) Specific video identification via joint learning of latent semantic concept, scene and temporal structure”. All these 38 papers cover a wide range of methods and applications about bridging the semantic gap in multimedia understanding. This special issue serves as a forum for researchers all over the world to discuss their works and recent advances in learning with semantic information methods and its applications in multimedia analysis. We hope this issue appeals to both the experts in the field and those who wish a snapshot of the current breadth of practical multimedia understanding.
Yan Yan Department of Information Engineering and Computer Science, University of Trento, Trento 38123, Italy Jiwen Lu Department of Automation, Tsinghua University, Beijing 100084, China
Received 13 May 2016; accepted 14 May 2016 Available online 4 June 2016
Contents lists available at ScienceDirect
Neurocomputing journal homepage: www.elsevier.com/locate/neucom
Guest editorial: Bridging the semantic gap in multimedia understanding
1. Guest editorial for the issue The explosive growth of visual and textual data, both on the Web and stored in private repositories, has led to urgent requirements in terms of search, processing and management of digital content. Developing optimal solutions to allow access to and mining such data is crucial. For image and video understanding, due to the unconstrained nature of images and videos, and the lack of fully reliable low-level features, the process of image and video understanding can be helped by grounding it with a prior semantic model describing any domain knowledge, which may operate during both learning and inference. An important step for Artificial Intelligence is to bridge the semantic gap between vision and language. This special issue serves as a forum for researchers all over the world to discuss their works and recent advances in learning with semantic information methods and its applications in multimedia analysis. This issue consists of 38 papers, which are briefly discussed as follows. First of all, to provide readers of the special issue with a stateof-the-art background on the topic of learning with semantic information, one survey paper “On interactive learning-to-rank for IR: Overview, recent advances, challenges, and directions” is included in this special issue. There are two papers addressed the topic of ontology design and semantic concept detection in this issue, i.e., (1) “Contextaware ontologies generation with basic level concepts from collaborative tags”, (2) “Multi-source alert data understanding for security semantic discovery based on rough set theory”. Image and video search and retrieval is very useful in our daily life now. This issue consists of five papers about this topic, i.e., (1) “Hierarchical learning of large-margin metrics for large-scale image classification”, (2) “Binary code learning via optimal class representations”, (3) “A correlation graph approach for unsupervised manifold learning in image retrieval tasks”, (4) “An image classification method that considers privacy-preservation”, (5) “A novel dynamic multi-model relevance feedback procedure for contentbased image retrieval”. Learning with semantic information with deep learning framework is a hot topic these days. This issue consists of two papers
http://dx.doi.org/10.1016/j.neucom.2016.05.051 0925-2312/& 2016 Elsevier B.V. All rights reserved.
about this topic, i.e., (1) “People counting based on head detection combining Adaboost and CNN in crowded surveillance environment”, (2) “Research on the natural image super-resolution reconstruction algorithm based on compressive perception theory and deep learning model”. Novel machine learning approach for vision-language integration is important for bridging the semantic gap in multimedia understanding. This issue consists of eleven papers about this topic, i.e., (1) “A classification model for semantic entailment recognition with feature combination”, (2) “Multi-view semisupervised learning for image classification”, (3) “Semi-supervised subspace learning with L2graph”, (4) “On the distance metric learning between cross-domain gaits”, (5) “Transfer subspace learning for cross-dataset facial expression recognition”, (6) “A Laplacian structured representation model in subspace clustering for enhanced motion segmentation”, (7) “Discriminative sparse projections for activity-based Person recognition”, (8) “A framework of uniform contribution embedding of data”, (9) “Biased subspace learning for misalignment-robust facial expression recognition”, (10) “Clustering by fast search and find of density peaks via heat diffusion”, (11) “Hybrid generative-discriminative learning for online tracking of sperm cell”. Moreover, there are many interesting real-world multimedia applications based on learning with semantic information. This issue consists of seventeen papers about this topic, i.e., (1) “Breaking down violence detection: Combining divide-et-impera and coarseto-fine strategies”, (2) “Sky detection by effective context inference”, (3) “Age progression: Current technologies and applications”, (4) “Joint local regressors learning for face alignment”, (5) “Effect of variation in gesticulation pattern in dynamic hand gesture recognition system”, (6) “Local surface geometric feature for 3D human action”, (7) “Evaluation of Kinect2 based balance measurement”, (8) “Spatial and temporal scoring for egocentric video summarization”, (9) “Robust plant cell tracking using local spatial-temporal context”, (10) “Incremental image set querying based localization”, (11) “Reduce false positives for object detection by a priori probability in videos”, (12) “Bridge the semantic gap between pop music feature and emotion: Build an interpretable model”, (13) “Visual homograph: A novel basic visual element”, (14) “Building change detection with RGB-D map generated from UAV images”, (15) “Multi-
2
Y. Yan and J. Lu / Neurocomputing 208 (2016) 1–2
target tracking via hierarchical association learning”, (16) “Exponential discriminant locality preserving projection for face recognition, (17) Specific video identification via joint learning of latent semantic concept, scene and temporal structure”. All these 38 papers cover a wide range of methods and applications about bridging the semantic gap in multimedia understanding. This special issue serves as a forum for researchers all over the world to discuss their works and recent advances in learning with semantic information methods and its applications in multimedia analysis. We hope this issue appeals to both the experts in the field and those who wish a snapshot of the current breadth of practical multimedia understanding.
Yan Yan Department of Information Engineering and Computer Science, University of Trento, Trento 38123, Italy Jiwen Lu Department of Automation, Tsinghua University, Beijing 100084, China
Received 13 May 2016; accepted 14 May 2016 Available online 4 June 2016