Publications

Preprints and working documents

  1. L. Herranz, W. Min, S. Jiang, “Food recognition and recipe analysis: integrating visual content, context and external knowledge”, arXiv preprint , Jan. 2018 [arxiv].

International journals

  1. Y. Wang, F. Yang, L. Murn, J. Sock, M. Górriz-Blanch, S. Wan, W. Zhang, F. Yang, L. Herranz, “Enhanced Neural Video Compression for Cloud Gaming Videos with Aligned Frame Generation”, Expert Systems With Applications (accepted) [link].
  2. Z. Liu, F. Yang, D. Wang, M. Górriz-Blanch, L. Murn, S. Wan, S. Zhang, M. Mrak, L. Herranz, “A slimmable framework for practical neural video compression”, Neurocomputing, vol. 610, 128525, Dec. 2024 [link].
  3. C. Zou, S. Wan, M. Górriz Blanch, L. Murn, M. Mrak, J. Sock, F. Yang, L. Herranz, “Lightweight Deep Exemplar Colorization via Semantic Attention-Guided Laplacian Pyramid”, IEEE Trans. on Visualization and Computer Graphics, 2024 (accepted) [link].
  4. D. Xue, J. Vázquez-Corral, L. Herranz, Y. Zhang, M.S. Brown, “Palette-based Color Harmonization via Color Naming”, IEEE Signal Processing Letters, v. 31, pp. 1474 – 1478, May 2024 [link].
  5. J. Vázquez-Corral, Graham D. Finlayson, L. Herranz, “Improving the perception of low-light enhanced images”, Optics Express, vol. 32, no. 4, pp. 5174-5190, Jan. 2024 [link].
  6. Y. Wang, A. González-García, C. Wu, L. Herranz, F.S. Khan, S. Jui, J. Yang, J. van de Weijer, “MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains”, International Journal of Computer Vision, vol. 132, pp. 490–514, 2024 [arxiv] [link].
  7. C. Zou, S. Wan, T. Ji, M. Górriz-Blanch, M. Mrak, L. Herranz, “Chroma Intra Prediction with Lightweight Attention-Based Neural Networks”, IEEE Trans. on Circuits and Systems for Video Technology, vol. 34, no. 1, pp. 549-560, Jan. 2024 [link].
  8. V.O. Yazici, L. Yu, A. Ramisa, L. Herranz, J. van de Weijer, “Main Product Detection with Graph Networks for Fashion”, Multimedia Tools and Applications, vol. 83, no. 1, pp. 3215-3231, 2024 [arxiv] [link].
  9. M. Yang, F. Yang, L. Murn, M. Gorriz-Blanch, J. Sock, S. Wan, F. Yang, L. Herranz, “Task-Switchable Pre-Processor for Image Compression for Multiple Machine Vision Tasks”, IEEE Trans. on Circuits and Systems for Video Technology, v. 34, no. 7, pp. 6414-6429, July 2024 [link].
  10. D. Xue, J. Vázquez-Corral, L. Herranz, Y. Zhang, M. Brown, “A Palette-based Image Recoloring Framework for Multiple-image Color Consistency”, Computer Graphics Forum, vol. 42, no. 7, 2023 [link].
  11. S. Yang, Y. Wang, J. van de Weijer, L. Herranz, S. Jui, J. Yang, “ Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol 45, no. 12, pp. 15883-15895, Dec. 2023 [arxiv] [link] [blog].
  12. S. Yang, Y. Wang, J. van de Weijer, L. Herranz, S. Jui, “Casting a BAIT for Offline and Online Source-free Domain Adaptation”, Computer Vision and Image Understanding, vol. 234, 103747, Sept. 2023 [arxiv] [link] [blog].
  13. F. Yang, Y. Wang, L. Herranz, Y. Cheng, M. Mozerov, “A Novel Framework for Image-to-image Translation and Image Compression”, Neurocomputing, vol. 508, pp. 58-70, October 2022 [arxiv] [link].
  14. K. Wang, L. Herranz, J. van de Weijer, “ACAE-REMIND for Online Continual Learning with Compressed Feature Replay”, Pattern Recognition Letters, v. 150, pp. 122-129, Oct. 2021 [arxiv] [link] [talk].
  15. S. Yang, K. Wang, L. Herranz, J. van de Weijer, “On Implicit Attribute Localization for Zero-Shot Learning”, IEEE Signal Processing Letters, vol. 28, pp. 872-876, April 2021 [arxiv] [link].
  16. S. Katakol, B. Elbarashy, L. Herranz, J. van de Weijer, A. M. Lopez, “Distributed Learning and Inference with Compressed Images”, IEEE Transactions on Image Processing, vol. 30, pp. 3069-3083, Feb. 2021 [arxiv] [link] [slides] [blog​​].
  17. Y. Wang, A. González-García, L. Herranz, J. van de Weijer, “Controlling biases and diversity in diverse image-to-image translation”, Computer Vision and Image Understanding , Jan. 2021 [arxiv] [link].
  18. Y. Wang, L. Herranz, J. van de Weijer, “Mix and match networks: multi-domain alignment for unpaired image-to-image translation”, International Journal of Computer Vision, vol. 128, no. 12, pp. 2849–2872, Dec. 2020 [arxiv] [link].
  19. X. Li, L. Herranz, S. Jiang, “Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition”, ACM Transactions on Data Science, March 2020 [arxiv] [link].
  20. F. Yang, L. Herranz, J. van de Weijer, J.A. Iglesias Guitián, A. López, M. Mozerov, “Variable Rate Deep Image Compression with Modulated Autoencoders”, IEEE Signal Processing Letters, Jan. 2020 [arxiv] [link] [blog​​].
  21. X. Song, S. Jiang, L. Herranz, C. Chen, “Learning Effective RGB-D Representations for Scene Recognition” , IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 980-993, Feb. 2019 [arxiv] [link] [blog].
  22. W. Min, B. Bao, Q. Xu, L. Herranz, S. Jiang, “Editorial Note: Efficient Multimedia Processing Methods and Applications” , Multimedia Tools and Applications, vol. 78, no. 1, Jan. 2019 [link].
  23. W. Min, S. Jiang, S. Wang, R. Xu, Y. Cao, L. Herranz, Z. He, “A Survey on Context-aware Mobile Visual Recognition”, Multimedia Systems vol. 75, no. 7, pp. 3933–3936, Nov. 2017  [link].
  24. X. Song, S. Jiang, L. Herranz, “Multi-scale multi-feature context modeling for scene recognition in the semantic manifold”, IEEE Transactions on Image Processing, vol. 26 no. 6, pp. 2721-2735, June 2017  [link].
  25. W. Min, S. Jiang, J. Sang, H. Wang, L. Herranz, “Being a Super Cook: Joint Food Attributes and Multi-Modal Content Modeling for Recipe Retrieval and Exploration”, IEEE Transactions on Multimedia, vol. 19 no. 5, 1100-1113, May 2017 [link] [dataset].
  26. L. Herranz, S. Jiang, R. Xu, “Modeling Restaurant Context for Food Recognition”, IEEE Transactions on Multimedia , vol. 19, no. 2, pp. 430-440, Feb. 2017  [link].
  27. X. Song, S. Jiang, L. Herranz, Y. Kong, K. Zheng, “Category co-occurrence modeling for large scale scene recognition”, Pattern Recognition, vol. 59, pp. 98-111, Nov. 2016 [link] [poster].
  28. L. Herranz, S. Jiang, “Scalable storyboards in handheld devices: applications and evaluation metrics”, Multimedia Tools and Applications, vol. 75, no. 20, pp. 12597-12625, October 2016 [link].
  29. L. Herranz, J. Cheng, Y. Gao, S. Jiang, “Guest Editorial: Image Analysis and Processing Leveraging Additional Information”, Multimedia Tools and Applications, vol. 75 no. 7, 3933-3936, March 2016 [link].
  30. R. Xu, L. Herranz, S. Jiang, S. Wang, X. Song, R. Jain, “Geolocalized Modeling for Dish Recognition”, IEEE Transactions on Multimedia, vol. 17, no. 8, pp. 1187-1199, August 2015 [link] [poster] [dataset].
  31. X. Lv, S. Jiang, L. Herranz, S. Wang, “RGB-D Hand-held Object Recognition based on Heterogeneous Feature Fusion”, Journal of Computer Science and Technology, vol. 30, no. 2, pp. 340-352, March 2015 [link].
  32. L. Herranz, J.M. Martínez, “Combining MPEG tools to generate video summaries adapted to the terminal and network”, Computer Journal, vol. 56, no. 5, pp. 529-553, May 2013   [link].
  33. L. Herranz, J. Calic, J. M. Martínez, M. Mrak, “Scalable Comic-Like Video Summaries and Layout Disturbance”, IEEE Transactions on Multimedia, vol. 14, no. 4, pp. 1290-1297, August 2012 [link].
  34. L. Herranz, J. M. Martínez, “A framework for scalable summarization of video”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no. 9, pp. 1265-1270, September 2010 [link].
  35. L. Herranz, J. M. Martínez, “On the use of hierarchical prediction structures for efficient summary generation of H.264/AVC bitstreams”, Signal Processing: Image Communication, vol. 24, no. 8, pp. 615-629, September 2009  [link].
  36. L. Herranz and J. M. Martínez, “An integrated approach to summarization and adaptation using H.264/MPEG-4 SVC”, Signal Processing: Image Communication, vol. 24, no. 6, pp. 499-509, July 2009  [link].
  37. L. Herranz, “Integrating semantic analysis and scalable video coding for efficient content-based adaptation”, Multimedia Systems, vol. 13, no. 2, pp. 103-118, August 2007 [link].
  38. J. Bescós, J.M. Martínez, L. Herranz, F. Tiburzi, “Content-driven adaptation of on-line video”, Signal Processing: Image Communication, vol. 22, no. 7, pp. 103-118, August-September 2007 [link].
  39. M. Padilla, J.M. Martínez, L. Herranz, “Video summaries generation and access via personalized delivery of multimedia presentations adapted to service and terminal”, International Journal of Intelligent Systems, vol. 21, no. 7, pp. 785-800, July 2006 [link].

International conferences and workshops

  1. D. Serrano-Lozano, L. Herranz, M. S. Brown, J. Vazquez-Corral, “Learned Image Enhancement via Color Naming”, Proc. European Conference on Computer Vision (ECCV24), Milano, Italy, Oct. 2024 [arxiv] [web] [code] [poster] [video].
  2. D. Xue, L. Herranz, J. Vázquez-Corral, Y. Zhang, “Burst Perception-Distortion Tradeoff: Analysis and Evaluation”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Rhodes Island, Greece, June 2023.
  3. M. Yang, L. Herranz, F. Yang, L. Murn, M. Górriz-Blanch, S. Wan, F. Yang, M. Mrak, “Semantic preprocessor for image compression for machines”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Rhodes Island, Greece, June 2023.
  4. Y. Wang, L. Murn, L. Herranz, F. Yang, M. Mrak, W. Zhang, S. Wan, M. Górriz-Blanch, “Efficient super-resolution for compression of gaming videos”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Rhodes Island, Greece, June 2023.
  5. D. Xue, F. Yang, P. Wang, L. Herranz, J. Sun, Y. Zhu, Y. Zhang, “SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision”, ACM Multimedia 2022, Lisbon, Oct. 2022. [arxiv] [poster] [​​video​​]
  6. C. Zou, S. Wan, M. Mrak, M. Gorriz-Blanch, L. Herranz, T. Ji, “Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding”, IEEE International Conference on Image Processing (ICIP2022), Bordeaux, Oct. 2022.
  7. K. Wang, X. Liu, A. Bagdanov, L. Herranz, S. Jui, J. van de Weijer, “Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition”, CVPR Workshop in Continual Learning in Computer Vision (CLVISION 2022), June 2022 [arxiv].
  8. Z. Liu, L. Herranz, F. Yang, S. Zhang, S. Wan, M. Mrak, M. Gorriz-Blanch, “Slimmable Video Codec”, CVPR Workshop and Challenge on Learned Image Compression (CLIC 2022), June 2022 [arxiv] [slides] [talk].
  9. S. Zhang, L. Herranz, M. Mrak, M. Gorriz-Blanch, S. Wan, F. Yang, “DCNGAN: A deformable convolution-based GAN with QP adaptation for perceptual quality enhancement of compressed video”, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022), May 2022 [arxiv].
  10. Shiqi Yang, Y. Wang, J. van de weijer, L. Herranz, S. Jui, “Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation”, Proc. Neural Information Processing Systems (NeurIPS21), December 2021 [arxiv] [slides] [blog].
  11. S. Zhang, M. Mrak, L. Herranz, M. Gorriz Blanch, S. Wan, F. Yang, “DVC-P: Deep Video Compression with Perceptual Optimizations”, Proc. Visual Communications and Image Processing (VCIP 2021) Conference, Munich, Germany, Dec. 2021.
  12. C. Zou, S. Wan, T. Ji, M. Mrak, M. Gorriz Blanch, L. Herranz, “Spatial Information Refinement for Chroma Intra Prediction in Video Coding”, Proc. Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA21), Tokyo, Japan, Dec. 2021.
  13. K. Wang, X. Liu, L. Herranz, J. van de Weijer, “HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification”, Proc. British Machine Vision Conference (BMCV21), Nov. 2021 [arxiv].
  14. S. Yang, Y. Wang, J. van de Weijer, L. Herranz, S. Jui, “Generalized Source-free Domain Adaptation”, Proc. International Conference on Computer Vision (ICCV21), October 2021 [arxiv] [poster] [project] [blog].
  15. K. Wang, L. Herranz, J. van de Weijer, “Continual learning in cross-modal retrieval”, CVPR Workshop in Continual Learning in Computer Vision (CLVISION 2021), June 2021 [arxiv] [slides] [​​video​​].
  16. S. Katakol, L. Herranz, F. Yang, M. Mrak, “DANICE: Domain adaptation without forgetting in neural image compression”, CVPR Workshop and Challenge on Learned Image Compression (CLIC 2021), June 2021 [arxiv] [slides] [​​video​​] [blog​​].
  17. F. Yang, L. Herranz, Y. Cheng, M. Mozerov, “Slimmable compressive autoencoders for practical neural image compression”, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR21), June 2021 [arxiv] [slides] [​​video​​] [blog​​].
  18. K. Wang, L. Herranz, A. Dutta, J. van de Weijer, “Bookworm continual learning: beyond zero-shot learning and continual learning”, Transferring and Adapting Source Knowledge in Computer Vision and VisDA Challenge (TASK-CV 2020) , June 2020 [arxiv] [talk] [slides].
  19. X. Song, H. Zeng, S. Zhang, L. Herranz, S. Jiang, “Generalized Zero-shot Learning with Multi-source Semantic Embeddings for Scene Recognition”, ACM Multimedia 2020, Oct. 2020 (acceptance rate 27.8%) [pdf] [slides].
  20. X. Liu, C. Wu, M. Menta, L. Herranz, B. Raducanu, A. D. Bagdanov, S. Jui, J. van de Weijer, “Generative Feature Replay For Class-Incremental Learning”,CVPR Workshop on Continual Learning on Computer Vision (CLVISION 2020), June 2020 [arxiv].
  21. Y. Wang, A. Gonzalez-Garcia, D. Berga, L. Herranz, F.S. Khan, J. van de Weijer, “MineGAN: effective knowledge transfer from GANs to target domains with few images”, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR20), June 2020 (acceptance rate 25.2%) [arxiv].
  22. L. Yu, B. Twardowski, X. Liu, L. Herranz, K. Wang, Y. Cheng, S. Jui, J. van de Weijer, “Semantic Drift Compensation for Class-Incremental Learning of Embeddings”, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR20), June 2020 (acceptance rate 25.2%) [arxiv].
  23. Y. Wang, A. González-García, J. van de Weijer, L. Herranz, “SDIT: Scalable and Diverse Cross-domain Image Translation”, ACM Multimedia 2019, Nice, France, Oct. 2019 [link] [arxiv].
  24. H. Prol, V. Dumoulin, L. Herranz, “Cross-Modulation Networks For Few-Shot Learning”, NeurIPS Workshop on Meta-Learning (MetaLearn 2018), Montreal, Canada, Dec. 2018 [arxiv] [code] [poster].
  25. C. Wu, L. Herranz, X. Liu, Y. Wang, J. van de Weijer, B. Raducanu, “Memory Replay GANs: learning to generate images from new categories without forgetting”, Proc. Neural Information Processing Systems (NeurIPS), Montreal, Canada, Dec. 2018 [link] [arxiv] [code] [slides] [blog].
  26. O. Caglayan, A. Bardet, F. Bougares, L. Barrault, K. Wang, M. Masana, L. Herranz, J. van de Weijer, “LIUM-CVC Submissions for WMT18 Multimodal Translation Task”, Proc. Conference on Machine Translation (WMT18), Brussels, Belgium, Oct. 2018 [link] [arxiv].
  27. Y. Wang, C. Wu, L. Herranz, J. van de Weijer, A. González-García, B. Raducanu, “Transferring GANs: generating images from limited data”, Proc. European Conference on Computer Vision (ECCV18), Munich, Germany, Sept. 2018 [arxiv].
  28. X. Liu, M. Masana, L. Herranz, J. Van de Weijer, A. M. Lopez, A. D. Bagdanov, “Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting”, Proc. International Conference on Pattern Recognition, Beijing, China, Aug. 2018 [arxiv] [slides] [blog].
  29. Y. Wang, J. van de Weijer, L. Herranz, “Mix and match networks: encoder-decoder alignment for zero-pair image translation”, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR18), Salt Lake City, Utah, USA, June 2018 [supp] [arxiv] [slides] [poster] [blog].
  30. M. Masana, J. van de Weijer, L. Herranz, A. D. Bagdanov, J. M. Álvarez, “Domain-adaptive deep network compression”, Proc. International Conference on Computer Vision (ICCV17), Venice, Italy, October 2017 (acceptance rate <29%) [arxiv] [code] [blog].
  31. O. Caglayan, W. Aransa, A. Bardet, M. García-Martínez, F. Bougares, L. Barrault, M. Masana, L. Herranz, J. van de Weijer, “LIUM-CVC Submissions for WMT17 Multimodal Translation Task”, Proc. Conference on Machine Translation (WMT17), Copenhagen, Denmark, July 2017 [link] [arxiv].
  32. X. Song, S. Jiang, L. Herranz, “Combining Models from Multiple Sources for RGB-D Scene Recognition”, Proc. International Joint Conference on Artificial Intelligence (IJCAI17), Melbourne, Australia, August 2017 (acceptance rate <26%) .
  33. X. Song, L. Herranz, S. Jiang, “Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs”, Proc. AAAI Conference on Artificial Intelligence (AAAI17), San Francisco, California, USA, February 2017 (acceptance rate <25%) [aaai] [poster] [models] [blog].
  34. X. Li, L. Herranz, Y. Zhao, S. Jiang, “Image captioning with both object and scene information”, Proc. ACM Multimedia 2016 Grand Challenge. 1st place in the YFGC image caption prediction challenge, Amsterdam, October 2016. [link]
  35. X. Li, L. Herranz, S. Jiang, “Heterogeneous convolutional neural networks for visual recognition”, Proc. Pacific-Rim Conference on Multimedia (PCM2016), Xi’an, China, September 2016 [link].
  36. L. Herranz, S. Jiang, X. Li, “Scene recognition with CNNs: objects, scales and dataset bias”, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR16), Las Vegas, Nevada, USA, June 2016 (acceptance rate 29.9%)[link] [poster].
  37. X. Lv, S. Jiang, L. Herranz, S. Wang: Hand-Object Sense: A Hand-held Object Recognition System Based on RGB-D Information. Proc. ACM Multimedia 2015: 765-766 [link].
  38. L. Herranz, R. Xu, S. Jiang, “A probabilistic framework for food recognition in restaurants”, Proc. International Conference on Multimedia and Expo 2015 (ICME15), pp. 1-6, Torino, Italy, June 2015 (oral acceptance rate 15%) [link] [slides] [poster].
  39. X. Song, S. Jiang, L. Herranz, “Joint Multi-feature Spatial Context for Scene Recognition on the Semantic Manifold”, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR15), pp. 1312-1320, Boston, Massachusetts, USA, June 2015 (acceptance rate 28.4%) [link] [poster].
  40. X. Song, S. Jiang, R. Xu, L. Herranz, “Semantic feature for food image recognition with geo-constraint”, Proc. International Conference on Data Mining (ICDM2014), Workshop on Social Multimedia Data Mining (SMMDM), pp. 1020 – 1025, Shenzhen, China, December 2014 [link].
  41. L. Herranz, S. Jiang, “Accuracy and specificity trade-off in k-nearest neighbors classification”, Proc. Asian Conference on Computer Vision (ACCV2014), LNCS, vol 9004, pp. 133-146, Singapore, Singapore, November 2014 [link].
  42. X. Li, S. Jiang, X. Song, L. Herranz, Z. Shi, “Multipath Convolutional-Recursive Neural Networks for Object Recognition”, Proc. International Conference on Intelligent Information Processing (IIP2014), pp. 269-277 , Hangzhou, China, October 2014 [link].
  43. C. Guo, L. Herranz, L. Wu, S. Jiang, “Region annotations in hashing based image retrieval”, Proc. International Conference on Internet Multimedia Computing and Service (ICIMCS), pp. 355-358, Xiamen, China, July 2014 [link].
  44. S. Zheng, L. Herranz, S. Jiang, “Flexible navigation in smartphones and tablets using scalable storyboards”, Proc. ACM International Conference on Multimedia Retrieval (ICMR2013), pp. 319-320, Dallas, Texas, USA, April 2013 [link].
  45. F. Wang, S. Jiang, L. Herranz, Q. Huang, “Improving Image Distance Metric Learning by Embedding Semantic Relations”, Proc. Pacific-Rim Conference on Multimedia (PCM2012), LNCS, vol 7674, pp. 424-434, Springer Verlag, Singapore, December 2012 [link].
  46. L. Herranz, H. Liu, S. Jiang, “Effective Comic-like Representations with Embedded Regions of Interest”, Proc. Pacific-Rim Conference on Multimedia (PCM2012), LNCS, vol 7674, pp. 464-475, Springer Verlag, Singapore, December 2012 [link].
  47. L. Herranz, “Multiscale browsing through video collections in smartphones using scalable storyboards”, Proc. International Conference on Multimedia and Expo 2012 (ICME12), Workshop on Social Multimedia Computing (SMC2012), pp. 278-283, Melbourne, Australia, July 2012 [link].
  48. L. Herranz, J. M. Martínez, “On the advantages of the use of bitstream extraction for video summary generation”,  Proc. International Conference on Multimedia Modeling (MMM10), LNCS, vol 5916, pp. 755-760, Chongqing, China, January 2010 [link].
  49. L. Herranz, J.M. Martínez, “An efficient summarization algorithm based on clustering and bitstream extraction”, Proc. International Conference on Multimedia and Expo 2009 (ICME09), pp. 654-657, New York, USA, Julio 2009  [link] [poster].
  50. L. Herranz, J.M. Martínez, “Generation of scalable summaries based on iterative GOP ranking”, Proc. International Conference on Image Processing (ICIP08), pp. 2544-2547, San Diego, California, October 2008 [link] [poster].
  51. L. Herranz, J.M. Martínez, “Integrated summarization and adaptation using H.264/MPEG-4 SVC”, Proc. International Conference on Visual Information Engineering (VIE08), pp. 729-734, Xi’an, China, July 2008 [link].
  52. L. Herranz, J.M. Martínez, “Use cases of scalable video based summarization within MPEG-21 DIA”, Proc. International Conference on Semantic and Digital Media Technology (SAMT07), LNCS, vol 4816, pp. 256-259, Springer Verlag, Genoa, Italy, December 2007 [link].
  53. J. Bescós, J.M. Martínez, L. Herranz, F. Tiburzi, “Content-driven adaptation of on-line video”, Proc. Intermational Workshop on Content Based Multimedia Indexing (CBMI07), pp. 8 pages, Bordeux, France, June 2007 [link].
  54. L. Herranz, J.M. Martínez, “Adapting surveillance video to small displays via object-based cropping”, Proc. International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS07), pp. 72-72, Santorini, Greece, June 2007 [link].
  55. L. Herranz, F. Tiburzi, J. Bescós, “Extraction of motion activity from scalable-coded video sequences”, Proc. International Conference on Semantic and Digital Media Technology (SAMT06), LNCS, vol 4306, pp. 148-158, Springer Verlag, Athens, Greece, December 2006 [link].
  56. L. Herranz, F. Tiburzi, J. Bescós, “An engine for content-aware on-line video adaptation”, Proc. International Conference on Semantic and Digital Media Technology (SAMT06), LNCS, vol 4306, pp. 101-112, Springer Verlag, Athens, Greece, December 2006 [link].
  57. L. Herranz, “A framework for online semantic adaptation of scalable video”, Proc. International Workshop on Semantic Media Adaptation and Personalization (SMAP06), pp. 13-18, Athens, Greece, December 2006 [link].
  58. L. Herranz, J. Bescós, “Reliability based optical flow estimation from MPEG compressed data”, Proc. International Workshop on Very Low Bitrate Video Coding (VLBV05), pp. 4 pages, Sardinia, Italy, September 2005.
  59. L. Herranz, J.M. Martínez, “An XSLT based framework integrating the search, filtering and retrieval of MPEG-7 descriptions”, Proc. Intermational Workshop on Content Based Multimedia Indexing (CBMI05), pp. 6 pages, Riga, Latvia, June 2005.
  60. J.M. Martínez, V. Valdés, J. Bescós, L. Herranz, “Introducing CAIN: a metadata-driven content adaptation manager integrating heterogeneous content adaptation tools”, Proc. International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS05), pp. 5 pages, Montreux, Switzerland, April 2005.
  61. J.M. Martínez, J. Bescós, V. Valdés, L. Herranz, “Integrating metadata-driven content adaptation approaches”, Proc. European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (EWIMT04), pp. 18-28, London, United Kingdom, November 2004.
  62. M. Padilla, J.M. Martínez, L.Herranz, “Universal and personalized access to video summaries via multimedia presentations in the DYMAS system”, Proc. International Workshop on Adaptive Multimedia Retrieval (AMR04), pp. 18-28, Valencia, Spain, August 2004.
  63. García, J.M., Martínez, L. Herranz, “Universal and personalized access to content via J2ME terminals in the DYMAS system”, Proc. Image and Video Retrieval (CIVR 2004), LNCS, vol 3115, pp. 225-233, Springer Verlag, Dublin, Ireland, June 2004 [link].
  64. L. Herranz, J.M. Martínez, “Towards universal and personalized access to audiovisual content in the DYMAS system”, Proc. International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS04), pp. 5 pages, Lisbon, Portugal, April 2004.

Book chapters

  1. L. Herranz, J. M. Martínez, “Using MPEG tools in video summarization”, The Handbook of MPEG Applications, ed. Wiley, pp. 125-149, November 2010 [link].

Theses

  1. L. Herranz, “A scalable approach to video summarization and adaptation”, Ph.D thesis, October 2010, Universidad Autónoma de Madrid [link] [slides].