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检索条件"主题词=feature coding"

共 68 条记录，以下是41-50 订阅

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SPATIAL PYRAMID ALIGNMENT FOR SPARSE coding BASED OBJECT CLASSIFICATION 24

SPATIAL PYRAMID ALIGNMENT FOR SPARSE CODING BASED OBJECT CLA...

引用

24th IEEE International Conference on Image Processing (ICIP)

作者： Kim, Joonsoo Tahboub, Khalid Delp, Edward J. Purdue Univ Sch Elect & Comp Engn Video & Image Proc Lab VIPER W Lafayette IN 47907 USA

ISBN: (纸本)9781509021758

The bag of visual words (BOW) model is widely used for image representation and classification. Spatial pyramid based feature pooling utilizes the BOW model and is the most popular approach to capture the spatial distribution (layout) of local image features, It makes the assumption that the center of an object is aligned with the center of an image, which can lead to misalignment and degradation in performance. In this paper, we propose a method to utilize max pooled features to estimate objects centers and align the spatial pyramid accordingly. We also propose an image representation descriptor robust to misalignments and objects deformations. The experimental results demonstrate that our spatial pyramid alignment method is simple yet efficient in handling misalignments and achieves high object classification accuracy.

关键词： object classification spatial pyramid feature coding spatial pyramid alignment

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Loss less Compression of Binary Image Descriptors for Visual Sensor Networks

Loss less Compression of Binary Image Descriptors for Visual...

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18th International Conference on Digital Signal Processing (DSP)

作者： Ascenso, Joao Pereira, Fernando Inst Super Engn Lisboa Inst Telecomun Lisbon Portugal Inst Super Tecn Inst Telecommun Lisbon Portugal

ISBN: (纸本)9781467358057

Nowadays, visual sensor networks have emerged as an important research area for distributed signal processing, with unique challenges in terms of performance, complexity, and resource allocation. In visual sensor networks, the energy consumption must be kept low to extend the lifetime of each battery-operated camera node. Thus, considering the large amount of data that visual sensors can generate, all the sensing, processing, and transmission operations must be optimized considering strict energy constraints. In this paper, camera nodes sense the visual scene but instead of transmitting the pixel coded representation, which demands high computation and bandwidth, a compact but yet rich visual representation is created and transmitted. This representation consists of discriminative visual features offering tremendous potential for several image analysis tasks. From all low-level image features available, the novel class of binary features, very fast to compute and match, are well suited for visual sensor networks. In this paper, lossless compression of binary image features is proposed to further lower the energy and bandwidth requirements. The coding solution exploits the redundancy between descriptors of an image by sorting the descriptors and applying DPCM and arithmetic coding. Experimental results show improvements up to 32% in terms of bitrate savings without any impact in the final image retrieval task accuracy.

关键词： visual sensor networks feature coding binary features image retrieval

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Locally Linear Salient coding for Image Classification

Locally Linear Salient Coding for Image Classification

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12th International Workshop on Content-Based Multimedia Indexing (CBMI)

作者： Babaee, Mohammadreza Rigoll, Gerhard Bahmanyar, Reza Datcu, Mihai Tech Univ Munich Inst Human Machine Commun D-80290 Munich Germany German Aerosp Ctr DLR Remote Sensing Technol Inst IMF Oberpfaffenhofen Germany

ISBN: (纸本)9781479939909

Representing images with their descriptive features is the fundamental problem in CBIR. feature coding as a key-step in feature description has attracted the attentions in recent years. Among the proposed coding strategies, Bag-of-Words (BoW) is the most widely used model. Recently saliency has been mentioned as the fundamental characteristic of BoW. Base on this idea, Salient coding (SaC) has been introduced. Empirical studies show that SaC is not able to represent the global structure of data with small number of codewords. In this paper, we remedy this limitation by introducing Locally Linear Salient coding (LLSaC). This method discovers the global structure of the data by exploiting the local linear reconstructions of the data points. This knowledge in addition to the salient responses, provided by SaC, helps to describe the structure of the data even with a few codewords. Experimental results show that LLSaC obtains state-of-the-art results on various data types such as multimedia and Earth Observation.

关键词： Content-Based Image Retrieval feature coding Salient coding Locally Linear Embedding

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FLEXIBLE RATE ALLOCATION FOR LOCAL BINARY feature COMPRESSION 25

FLEXIBLE RATE ALLOCATION FOR LOCAL BINARY FEATURE COMPRESSIO...

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25th IEEE International Conference on Image Processing (ICIP)

作者： Van Opdenbosch, Dominik Steinbach, Eckehard Tech Univ Munich Chair Media Technol Munich Germany

ISBN: (纸本)9781479970612

Numerous real-time applications in computer vision rely on finding correspondences between local binary features. In many mobile scenarios, the visual information captured at a sensor node needs to be transmitted to a processing server, which is capable of storing the visual information or executing a complex analysis task. However, not necessarily all the visual information need to be transmitted. In this paper, we present a rate allocation scheme that is capable of categorizing features into classes according to their usefulness and select the amount of data spent on each class to maximize the overall performance of a computer vision task. We demonstrate the approach using ORB, BRISK, and FREAK features and show the improvements on a homography estimation task.

关键词： Visual features Bag-of-Words coding feature coding ATC

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IMAGE CLASSIFICATION USING RBM TO ENCODE LOCAL DESCRIPTORS WITH GROUP SPARSE LEARNING

IMAGE CLASSIFICATION USING RBM TO ENCODE LOCAL DESCRIPTORS W...

引用

IEEE International Conference on Image Processing (ICIP)

作者： Wang, Jinzhu Wang, Wenmin Wang, Ronggang Gao, Wen Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Shenzhen Peoples R China Peking Univ Natl Engn Lab Video Technol Beijing Peoples R China

ISBN: (纸本)9781479983391

This paper proposes to employ deep learningmodel to encode local descriptors for image classification. Previous works using deep architectures to obtain higher representations are often operated from pixel level, which lack the power to be generalized to large-size and complex images due to computational burdens and internal essence capture. Our method slips the leash of this limitation by starting from local descriptors to leverage more semantical inputs. We investigate to use two layers of Restricted Boltzmann Machines (RBMs) to encode different local descriptors with a novel group sparse learning (GSL) inspired by the recent success of sparse coding. Besides, unlike the most existing pure unsupervised feature coding strategies, we use another RBM corresponding to semantic labels to perform supervised fine-tuning which makes our model more suitable for classification task. Experimental results on Caltech-256 and Indoor-67 datasets demonstrate the effectiveness of our method.

关键词： Image Classification feature coding Restricted Boltzmann Machine (RBM) Group Sparse Learning (GSL)

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MULTI-VIEW DISTRIBUTED SOURCE coding OF BINARY featureS FOR VISUAL SENSOR NETWORKS 41

MULTI-VIEW DISTRIBUTED SOURCE CODING OF BINARY FEATURES FOR ...

引用

41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Monteiro, Nuno Brites, Catarina Pereira, Fernando Ascenso, Joao Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9781479999880

Visual analysis algorithms have been mostly developed for a centralized scenario where all visual data is acquired and processed at a central location. However, in visual sensor networks (VSN), several constraints in computational power, energy and bandwidth require a radically different approach, notably a paradigm shift from centralized to distributed visual processing. In the new paradigm, visual data is acquired and features are extracted at the sensing nodes locations to be after transmitted to enable further analysis at some central location. In such scenario, one of the key challenges is to design suitable feature coding schemes that are able to exploit the correlation among the features corresponding to (partially) overlapped views of the same visual scene. To achieve efficient coding, it is proposed to employ the distributed source coding paradigm as it does not require any communication between the sensing nodes (rather expensive in VSN) and it is parsimonious in terms of computational resources. Experimental results show that significant accuracy and compression gains (up to 37.36%) can be achieved when coding features extracted from multiple views.

关键词： distributed source coding feature coding multi-view coding visual sensor networks

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Scene Image Classification Based on Improved VLAD Reprensentation

Scene Image Classification Based on Improved VLAD Reprensent...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Zhiyi Long, Xianzhong Li, Yun Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Sch Software Nanjing 210023 Peoples R China

ISBN: (纸本)9780738133669

Vector of Locally Aggregated Descriptors (VLAD) method, which aggregates descriptors and produces a compact image representation, has achieved great success in the field of image classification and retrieval. However, the original VLAD method is a hard assignment strategy that only assigns each descriptor to the nearest neighbor visual word in dictionary, which leads to large quantization error. In this paper, improved VLAD based on adaptive bases and saliency weights is proposed to solve the above problem. The new method considers the local density distribution when assigning local descriptors, adaptively selects several nearest neighbor visual words, and takes the coding coefficients obtained by utilizing saliency as the weights of the selected visual words. Experimental results on Corel 10, 15 Scenes and UIUC Sports Event datasets show that the new coding method proposed in this paper achieves better classification performance compared with the existing five VLAD based methods and two commonly used representation methods.

关键词： Image Classification feature coding VLAD Adaptive Bases Saliency Weights

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Encoding the Regional features for Person Re-identification using Locality-constrained Linear coding

Encoding the Regional Features for Person Re-identification ...

引用

International Conference on Computers, Communications and Systems (ICCCS)

作者： Li, Junnan Yang, Zhen Xiong, Huilin Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (纸本)9781467397544

This paper presents a coding method for person re-identification, based on Locality-constrained Linear coding (LLC), utilizing the locality constraints to project each descriptor into its local-coordinate system, in which a spatially weighted color feature combining with the HoG descriptor is introduced. The proposed encoding feature for LLC retains the basic form of Iterative re-weighting Sparse Ranking (ISR), and the final classification is directly made according to a normalized reconstruction error. Compared with other approaches, the proposed method avoids extracting sophisticated features and does not require the learning of a classifier. Experimental results show that the proposed approach improves rank-1 recognition accuracy on the CAVIAR4REID dataset from 29% to 31% with much lower computational costs.

关键词： person re-identification image classification feature coding LLC K-nearest-neighbor search

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Czech Text Steganography Method by Selective Hiding Technique

Czech Text Steganography Method by Selective Hiding Techniqu...

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World Congress on Engineering (WCE)

作者： Khan, Sungkrityayan Sankineni, Rishi Balagurunathan, Padmavathi Shree, Nujerlla Suresh Divya Balasubramanian, Abinaya SRM Univ Madras Tamil Nadu India SRM Univ Dept Comp Sci Madras Tamil Nadu India SRM Univ Dept Comp Sci & Engn Madras Tamil Nadu India

ISBN: (纸本)9789881925343

In this paper we have presented a novel approach of steganography which is suitable for Czech texts. The approach can be assorted under selective hiding method of text steganography. This approach involves the concealing of the information bits clandestinely within the letters gaining from their inherited points. To ascertain the specific letters holding the secluded bits, the propounded technique considers the two features, the redundant Czech extension characters and the extensions of the points in the letters. The un-pointed letters with extension are used by us to hold the secluded bit 'zero' and the pointed letters having an extension to hold the 'one' bit. This specific steganography technique is found similar to other languages having alike text to Czech such as Esperanto, Vietnamese etc.

关键词： Czech Text Text Steganography Information Security Cryptography Text watermarking feature coding

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Palm Vein for Efficient Person Recognition Based on 2D Gabor Filter

Palm Vein for Efficient Person Recognition Based on 2D Gabor...

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Conference on Biometric and Surveillance Technology for Human and Activity Identification X

作者： Wang, Jixing He, Yuqing Zhu, Jiadan Gao, Xinru Cui, Yongsheng Beijing Inst Technol Sch Optoelect Minist Educ China Key Lab Photoelect Imaging Technol & Syst Beijing 100081 Peoples R China

ISBN: (纸本)9780819495037

Palm vein recognition is a relatively new method in biometrics. This paper presents an effective palm vein feature extraction approach for improving the efficiency of palm vein identification. In this paper, relevant preprocessing steps as rotation and extraction of the Region of Interest are presented. In feature extraction, multiple 2D Gabor filters with 4 orientations are employed to extract the phase information on a palm vein image, which is then merged into unique feature according to an encoding rule. Hamming distance is used for vein recognition. Experiments are carried on a self-made palm vein database. Experimental results show that the method in this paper achieved a higher correct recognition rate and a faster speed.

关键词： Pattern recognition Palm vein Gabor filter feature coding Hamming distance

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