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检索条件"机构=Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems"

共 530 条记录，以下是211-220 订阅

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QUERY DIFFICULTY ESTIMATION VIA PSEUDO RELEVANCE FEEDBACK FOR IMAGE SEARCH

QUERY DIFFICULTY ESTIMATION VIA PSEUDO RELEVANCE FEEDBACK FO...

引用

IEEE International Conference on Multimedia and Expo

作者： Qianghuai Jia Xinmei Tian Tao Mei CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Microsoft Research

ISBN: (纸本)9781479947607

Query difficulty estimation (QDE) attempts to automatically predict the performance of the search results returned for a given query. QDE has been widely investigated in text document retrieval for many years. However, few research works have been explored in image retrieval. State-of-the-art QDE methods in image retrieval mainly investigate the statistical characteristics (coherence, robustness, etc.) of the returned images to derive a value for indicating the query difficulty degree. To the best of our knowledge, little research has been done to directly estimate the real retrieval performance of the search results, such as average precision, instead of only an indicator. In this paper, we propose a novel query difficulty estimation approach which automatically estimate the average precision of the image search results. Specifically, we first select a set of query relevant and query irrelevant images for each query via pseudo relevance feedback. Then an efficient and effective voting scheme is proposed to estimate the relevance label of each image in the search results. Based on the images' relevance labels, the average precision of the search results returned for the given query is derived. The experimental results on a benchmark image search dataset demonstrate the effectiveness of the proposed method.

关键词： Query difficulty estimation (QDE) Image retrieval Average precision (AP) Pseudo relevance feedback (PRF) Voting scheme

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SIFT-preserving compression of mobile-captured license plate images for recognition

SIFT-preserving compression of mobile-captured license plate...

引用

International Conference on Wireless Communications and Signal processing (WCSP)

作者： Dandan Wang Dong Liu CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781479973408

Image compression had been extensively studied for reducing coding rate yet producing acceptable visual quality. However, there are many application scenarios where the compressed images are used for automatic recognition rather than human viewing, thus the visual quality is no longer critical for compression. SIFT features have demonstrated their utility in many recognition scenarios and SIFT-preserving compression is developed recently. In this paper, we firstly study the SIFT-preserving compression of license plate images for recognition accuracy rather than visual quality. According to extracted SIFT features, each image is divided into SIFT coding-units and non-SIFT coding-units. Each coding-unit is assigned with a different quality parameter when using JPEG for compression. We compare our proposed scheme with the standard JPEG that uses a unified quality parameter. Experimental results with manually tuned parameters show that on average 14% bit-rate can be saved by our scheme, without any loss of recognition accuracy.

关键词： Image coding Transform coding Licenses Standards Image recognition Feature extraction Accuracy

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M-LVC: Multiple Frames Prediction for Learned Video Compression

M-LVC: Multiple Frames Prediction for Learned Video Compress...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jianping Lin Dong Liu Houqiang Li Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

We propose an end-to-end learned video compression scheme for low-latency scenarios. Previous methods are limited in using the previous one frame as reference. Our method introduces the usage of the previous multiple frames as references. In our scheme, the motion vector (MV) field is calculated between the current frame and the previous one. With multiple reference frames and associated multiple MV fields, our designed network can generate more accurate prediction of the current frame, yielding less residual. Multiple reference frames also help generate MV prediction, which reduces the coding cost of MV field. We use two deep auto-encoders to compress the residual and the MV, respectively. To compensate for the compression error of the auto-encoders, we further design a MV refinement network and a residual refinement network, taking use of the multiple reference frames as well. All the modules in our scheme are jointly optimized through a single rate-distortion loss function. We use a step-by-step training strategy to optimize the entire scheme. Experimental results show that the proposed method outperforms the existing learned video compression methods for low-latency mode. Our method also performs better than H.265 in both PSNR and MS-SSIM. Our code and models are publicly available.

关键词： Video compression Image coding Motion compensation Entropy Encoding Motion estimation Transforms

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End-to-End Facial Image Compression with Integrated Semantic Distortion Metric

End-to-End Facial Image Compression with Integrated Semantic...

引用

IEEE Visual Communications and Image processing (VCIP)

作者： Tianyu He Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781538644591;9781538644584

High efficient facial image compression is broadly required and challenging for surveillance and security scenarios, while either traditional general image codecs or special facial image compression schemes only heuristically refine codec separately according to face verification accuracy metric. We propose an End-to-End Facial Image Compression (E2EFIC) framework with a novel variable block size Regionally Adaptive Pooling (RAP) module whose parameters can be automatically optimized according to gradient feedback from an integrated semantic distortion metrics, including a successful exploration to apply Generative Adversarial Network (GAN) as metric directly in image compression scheme. The experimental results verify the framework's efficiency by demonstrating performance improvement of 71.41%, 48.28% and 52.67% bitrate saving separately over JPEG2000, WebP and neural network-based codecs under the same face verification accuracy distortion metric. We also evaluate E2EFIC's superior performance gain compared with latest specific facial image codecs.

关键词： Image coding Distortion Semantics Face Bit rate Codecs

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Improving Semantic Segmentation via Label Propagation and Temporal Consistency

Improving Semantic Segmentation via Label Propagation and Te...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Feiyu Qin Lumeng Cao Xuejin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Semantic segmentation is a fundamental task in indoor scene understanding. Most previous supervised approaches rely on densely annotated image data sets. Due to the limited amount of images with segmentation labels, the performance of existing networks is greatly limited. In this paper, we exploit temporal correlation in video frames to improve the performance and robustness of segmentation networks. Two effective learning strategies are proposed to propagate the information from a few labeled frames to their immediate neighbor frames. First, we scale up training dataset for supervised semantic segmentation networks by generating pseudo ground-truth for neighboring frames from a labeled frame using filtered homography transformation. Furthermore, we introduce a self-supervised loss function to ensure temporal consistency between the segmentation results of adjacent frames. The experimental results demonstrate that our proposed method outperforms state-of-the-art techniques for semantic segmentation on NYU-Depth V2 dataset.

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Deep Scalable Image Compression via Hierarchical Feature Decorrelation

Deep Scalable Image Compression via Hierarchical Feature Dec...

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Picture Coding Symposium, PCS

作者： Zongyu Guo Zhizheng Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

The following topics are dealt with: video coding; data compression; image coding; convolutional neural nets; decoding; learning (artificial intelligence); motion compensation; video codecs; image reconstruction; filt...

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Edge-Guided Panoramic Video Stitching with Limited Overlap

Edge-Guided Panoramic Video Stitching with Limited Overlap

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Chaoyu Xie Xuejin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Video stitching remains a challenging problem in computer vision. In this paper, we propose a novel edge-guided method to stitch multiple videos that have small overlapped regions. Our algorithm consists of three steps: (1) spherical projection of the input video frames based on camera calibration, (2) edge detection and edge-guided feature matching for video registration, and (3) seam optimization to eliminate distortions and ghosts in the composited panoramic videos. The experimental results and user studies demonstrate that our method is robust to videos that have small overlapped regions and produces more visually pleasing panoramic videos than state-of-the-art techniques.

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A CNN-Based In-Loop Filter with CU Classification for HEVC

A CNN-Based In-Loop Filter with CU Classification for HEVC

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IEEE Visual Communications and Image processing (VCIP)

作者： Yuanying Dai Dong Liu Zheng-Jun Zha Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781538644591;9781538644584

Lossy compression of image and video yields visually annoying artifacts including blocking, blurring, ringing, etc., especially at low bit rates. In-loop filtering techniques can reduce these artifacts, improve quality, and achieve coding gain accordingly. In this paper, we present a convolutional neural network (CNN) based in-loop filter for High Efficiency Video Coding (HEVC). First, we design a new CNN structure that is composed of multiple Variable-filter-size Residue-learning blocks, namely VRCNN-ext, for artifact reduction. VRCNN-ext is trained by natural images as well as their compressed versions at different quality levels. Second, we investigate a new in-loop filter based on the trained VRCNN-ext models. Specifically, we observed that using VRCNN-ext directly on the inter pictures is not effective. To solve this problem, we further train a classifier to decide whether to use VRCNN-ext for each coding unit (CU). The classifier makes decision based on the compressed information, thus avoiding the overhead bits to control the on/off of the CNN-based filter at the CU level. Experimental results show that our scheme achieves significant bits saving than the HEVC anchor, leading to on average 9.2%, 9.6% and 7.4% BD-rate reduction on the HEVC test sequences, under all-intra, low-delay B and random-access configurations, respectively.

关键词： Decoding Encoding Training Image coding Feature extraction Copper Video coding

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360HRL: Hierarchical Reinforcement Learning Based Rate Adaptation for 360-Degree Video Streaming

360HRL: Hierarchical Reinforcement Learning Based Rate Adapt...

引用

IEEE Visual Communications and Image processing (VCIP)

作者： Jun Fu Chen Hou Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

Recently, reinforced adaptive bitrate (ABR) algorithms have achieved remarkable success in tile-based 360-degree video streaming. However, they heavily rely on accurate viewport prediction. To alleviate this issue, we propose a hierarchical reinforcement-learning (RL) based ABR algorithm, dubbed 360HRL. Specifically, 360HRL consists of a top agent and a bottom agent. The former is used to decide whether to download a new segment for continuous playback or re-download an old segment for correcting wrong bitrate decisions caused by inaccurate viewport estimation, and the latter is used to select bitrates for tiles in the chosen segment. In addition, 360HRL adopts a two-stage training methodology. In the first stage, the bottom agent is trained under the environment where the top agent always chooses to download a new segment. In the second stage, the bottom agent is fixed and the top agent is optimized with the help of a heuristic decision rule. Experimental results demonstrate that 360HRL outperforms existing RL-based ABR algorithms across a broad of network conditions and quality of experience (QoE) objectives.

关键词： Training Visual communication Image processing Bit rate Estimation Reinforcement learning Streaming media

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Semantically Scalable Image Coding With Compression of Feature Maps

Semantically Scalable Image Coding With Compression of Featu...

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IEEE International Conference on Image processing

作者： Ning Yan Dong Liu Houqiang Li Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

In this paper, we consider a novel image coding paradigm, termed semantically scalable coding. In the new paradigm, coded bitstream serves for multiple different semantic analysis tasks, and different tasks require different semantic granularities of the image. Thus, the bitstream is designed to be scalable in the sense that progressive decoding of the bitstream provides coarse-to-fine semantic granularities. As a concrete example, we consider the task of coarse-grained and fine-grained image classification. We present a method to compress the multiple deep feature maps that are intermediate representations of an image passing a trained deep network. The deep-layer feature maps can serve for coarse-grained image classification while the shallow-layer feature maps can serve for fine-grained image classification. Experimental results demonstrate the feasibility of the proposed method, as well as the advantage of the semantically scalable coding paradigm.

关键词： Image coding Task analysis Semantics Visualization Correlation Decoding Encoding

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