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检索条件"任意字段=Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing"

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Zero-shot Learning using Graph Regularized Latent Discriminative Cross-domain Triplets 11

Zero-shot Learning using Graph Regularized Latent Discrimina...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Gune, Omkar Vora, Meet Banerjee, Biplab Chaudhuri, Subhasis Indian Inst Technol Mumbai Maharashtra India Indian Inst Technol Roorkee Roorkee Uttar Pradesh India

ISBN: (纸本)9781450366151

Zero-shot learning (ZSL) for visual recognition aims at identifying the previously unseen class samples given a trained model on the labeled visual samples of seen classes and additional class-level semantic side information for all classes. Often ZSL is tackled by learning an embedding function from the visual to semantic space or vice-versa. However, learning this mapping often results in loss of discriminative property of learned embedding space, thus severely compromising the recognition performance on the test samples. In order to ensure improved discrimination in the embedding space, we introduce a ZSL framework by leveraging the intuitive idea of cross-domain triplets based metric learning for learning such a space. Additionally, we introduce a novel graph Laplacian based regularizer which aligns the graph structures of the visual and semantic spaces in the learned embedding space. Simultaneously optimizing both the criteria results in a compact, discriminative, and meaningful embedding space, which is experimentally found to be superior to most of its existing counterparts on both the standard ZSL (AwA and CUB) and the challenging generalized ZSL (AwA1, AwA2, CUB) settings.

关键词： Zero-shot learning cross-domain alignment

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1st International conference on Artificial Intelligence and Cognitive Computing, AICC 2018

1st International Conference on Artificial Intelligence and ...

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1st International conference on Artificial Intelligence and Cognitive Computing, AICC 2018

ISBN: (纸本)9789811315794

The proceedings contain 68 papers. The special focus in this conference is on Artificial Intelligence and Cognitive Computing. The topics include: Cognitive-based adaptive path planning for mobile robot in dynamic environment;prediction for indian road network images dataset using feature extraction method;Design and simulation of capacitive MEMS accelerometer;bioinformatics and image processing—Detection of plant diseases;hybrid approach for pixel-wise semantic segmentation using SegNet and squeezenet for embedded platforms;software modernization through model transformations;Semi-automatic annotation of images using eye gaze data (SAIGA);optimizing regression test suite reduction;application monitoring—Active learning approach;acquiring best rules that represent datasets;an automated computer vision system for extraction of retail food product metadata;attacks on two-key elliptic curve digital signature algorithm;bioinformatics: An application in information science;leveraging deep learning for anomaly detection in video surveillance;speaker diarization system using hidden Markov toolkit;Survey: Enhanced trust management for improving QoS in MANETs;a generic survey on medical big data analysis using internet of things;mean estimation under post-stratified cluster sampling scheme;privacy-preserving Naive Bayesian classifier for continuous data and discrete data;learning style recognition: A neural network approach;morphological-based localization of an Iris image;an enhanced efficiency of key management method for wireless sensor networks using mobile agents;a survey on emotion’s recognition using internet of things;KAZE feature based passive image forgery detection;efficiency-based analysis of homomorphic encryption implications;Performance and analysis of human attention using single-channel wireless EEG sensor for medical application;smart heartbeat monitoring system using machine learning.

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Anomaly Detection using Context Dependent Optical Flow 11

Anomaly Detection using Context Dependent Optical Flow

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Mondal, Ranjan Chanda, Bhabatosh Indian Stat Inst Kolkata India

ISBN: (纸本)9781450366151

Anomaly detection is a major task in crowd management through video surveillance. It refers to the events which are deviated from normal events. We have introduced an unsupervised method to detect motion anomaly in surveillance video. In this work we have considered only optical flow as feature. First, for each frame we compute magnitude of optical flow of motion using flownet2 [10]. Then, mean of magnitude of flow due to regular normal motion (given in training data) is computed at each pixel where such motion exists in the training video frames. Our strategy is to compare the motion under consideration against this mean flow magnitude, and we expect that the anomalous motion would differ significantly from normal motion. An autoencoder type network is trained to detect this anomaly. Training data patch is constructed by Interleaving the columns of mean optical flow patch and the corresponding flow patch from each frame. This interleaving is done to incorporate context dependency. The autoencoder is trained to minimize mean-square reconstruction error between input column wise interleaved patch and output (i.e., reconstructed patch) of the autoencoder. During testing, a patch is declared anomalous if the reconstruction error is high compared to the training error. Experiments have been carried out on UCSD and UMN dataset and are compared with other methods. Our method gives comparable results with other state-of-the-art methods.

关键词： Anomaly Detection Motion Representation Activity Recognition Unsupervised Method

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Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian 11

Seek and You Will Find: A New Optimized Framework for Effici...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Das, Sudip Mukherjee, Partha Sarathi Bhattacharya, Ujjwal Indian Stat Inst Kolkata India

ISBN: (纸本)9781450366151

Studies of object detection and localization, particularly pedestrian detection have received considerable attention in recent times due to its several prospective applications such as surveillance, driving assistance, autonomous cars, etc. Also, a significant trend of latest research studies in related problem areas is the use of sophisticated Deep Learning based approaches to improve the benchmark performance on various standard datasets. A trade-off between the speed (number of video frames processed per second) and detection accuracy has often been reported in the existing literature. In this article, we present a new but simple deep learning based strategy for pedestrian detection that improves this trade-off. Since training of similar models using publicly available sample datasets failed to improve the detection performance to some significant extent, particularly for the instances of pedestrians of smaller sizes, we have developed a new sample dataset consisting of more than 80K annotated pedestrian figures in videos recorded under varying traffic conditions. Performance of the proposed model on the test samples of the new dataset and two other existing datasets, namely Caltech Pedestrian Dataset (CPD) and CityPerson Dataset (CD) have been obtained. Our proposed system shows nearly 16% improvement over the existing state-of-the-art result.

关键词： Pedestrian Detection Real Time Detection Pedestrian Dataset Convolutional Neural Networks Deep Learning

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Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and Rotation Invariant Character Recognition 11

Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and R...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Swetha, V. C. Mishra, Deepak Gorthi, Sai Subrahmanyam Indian Inst Space Sci & Technol Thiruvananthapuram Kerala India Indian Inst Technol Tirupati Andhra Pradesh India

ISBN: (纸本)9781450366151

Last decade has witnessed rapid growth for the popularity of Convolutional Neural Networks (CNNs), in detecting and classifying objects. The self trainable nature of CNNs makes them the strongest candidate as a classifier and a feature extractor. However, many of the existing CNN architectures fail recognizing texts or objects under input rotation and scaling. This paper introduces an elegant approach, 'Scale and Rotation Corrected CNN (SRC-CNN)' for scale and rotation invariant text recognition, exploiting the concept of principal component of characters. Prior to training and testing with baseline CNN, 'SRC-CNN' maps each character image to a reference orientation and scale, which is again derived from the character image itself. SRC-CNN is capable of recognizing characters in a document, even though they differ in orientation and scale greatly. The proposed method does not demand any training with samples which are scaled or rotated. The performance of proposed approach is validated on different character data sets like MNIST, MNIST_rot_12k and English alphabets and compared with state of the art rotation invariant classification networks. SRC-CNN is a generalized approach and can be extended for rotation and scale invariant classification of many other datasets as well, choosing any appropriate baseline CNN. Here we have demonstrated the generality of the proposed SRC-CNN on MNIST Fashion data set and found to perform well in rotation and scale invariant classification of objects as well. This paper demonstrates how the basic PCA based rotation and scale invariant image recognition can be integrated to CNN for achieving better rotational and scale invariances in classification.

关键词： Character recognition

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Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification 11

Multiple Kernel Fisher Discriminant Metric Learning for Pers...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Ali, T. M. Feroz Velmurugan, Rajbabu Patel, Kalpesh K. Chaudhuri, Subhasis Indian Inst Technol Dept Elect Engn Mumbai Maharashtra India

ISBN: (纸本)9781450366151

Person re-identification addresses the problem of matching pedestrian images across disjoint camera views. Design of feature descriptor and distance metric learning are the two fundamental tasks in person re-identification. In this paper, we propose a metric learning framework for person re-identification, where the discriminative metric space is learned using Kernel Fisher Discriminant Analysis (KFDA), to simultaneously maximize the inter-class variance as well as minimize the intra-class variance. We derive a Mahalanobis metric induced by KFDA and argue that KFDA is efficient to be applied for metric learning in person re-identification. We also show how the efficiency of KFDA in metric learning can be further enhanced for person re-identification by using two simple yet efficient multiple kernel learning methods. We conduct extensive experiments on three benchmark datasets for person re-identification and demonstrate that the proposed approaches have competitive performance with state-of-the-art methods.

关键词： Person Re-identification Metric Learning Kernel Fisher Discriminant Analysis Multiple Kernel Fisher Discriminant Analysis

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Nrityantar: Pose oblivious indian classical dance sequence classification system 11

Nrityantar: Pose oblivious Indian classical dance sequence c...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Kaushik, Vinay Mukherjee, Prerana Lall, Brejesh IIT Delhi Dept Elect Engn Delhi India

ISBN: (纸本)9781450366151

In this paper, we attempt to advance the research work done in human action recognition to a rather specialized application namely indian Classical Dance (ICD) classification. The variation in such dance forms in terms of hand and body postures, facial expressions or emotions and head orientation makes pose estimation an extremely challenging task. To circumvent this problem, we construct a pose-oblivious shape signature which is fed to a sequence learning framework. The pose signature representation is done in two-fold process. First, we represent person-pose in first frame of a dance video using symmetric Spatial Transformer Networks (STN) to extract good person object proposals and CNNb-ased parallel single person pose estimator (SPPE). Next, the pose basis are converted to pose flows by assigning a similarity score between successive poses followed by non-maximal suppression. Instead of feeding a simple chain of joints in the sequence learner which generally hinders the network performance we constitute a feature vector of the normalized distance vectors, flow, angles between anchor joints which captures the adjacency configuration in the skeletal pattern. Thus, the kinematic relationship amongst the body joints across the frames using pose estimation helps in better establishing the spatio-temporal dependencies. We present an exhaustive empirical evaluation of state-of-the-art deep network based methods for dance classification on ICD dataset.

关键词： Pose Signature Dance classification Action Recognition Motion and Video Analysis Deep Learning Supervised Learning LSTM

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Video Dehazing using LMNN with respect to Augmented MRF 11

Video Dehazing using LMNN with respect to Augmented MRF

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Borkar, Kushal Mukherjee, Snehasis Indian Inst Informat Technol Sric Chittoor Andhra Pradesh India

ISBN: (纸本)9781450366151

The presence of haze within the atmospheric medium degrades the quality of videos captured by camera sensors. The expulsion of haze, referred to as dehazing, is typically performed subject to the physical degradation display, that involves an explanation of an ill-posed inverse drawback. A few efforts have been made for image dehazing, whereas, video dehazing still remains an unexplored area of research. This paper proposes an approach for video dehazing combining the concepts of single image dehazing, optical stream estimation and Markov Random Field (MRF). The proposed method enhances the temporal and spatial coherence of the hazy video. Assuming that the dark channel of the haze-free picture is zero, we acquire the raw transmission map. In the proposed approach, we focus on the raw transmission map obtained from the dark channel prior using guided filter. We assess the forward and reverse optical streams between the neighboring frames to locate individual pixels using Linear Discriminant Analysis. The color of the haze-free pixels in the frames is approximated by a few hundred discrete colors, which generate a fixed cluster in space and the directions of the pixel. The pixels at a given cluster are spread and can be determined by analyzing the forward and in reverse optical frames to predict its value after haze removal. Largest Margin Nearest Neighbor (LMNN) algorithm is applied to get the smooth transmission map of the foggy frames of the video to approximate the pixel value in the RGB space. The stream fields are utilized in an augmented MRF model on the transmission guide obtained to enhance the temporal and the spatial coherence of the transmission. The proposed method is compared against the state-of-the-art on both real and synthetic videos to preserve the information optimally.

关键词： video dehazing video-based modelling transmission estimation bayes largest margin nearest neighbor regularization

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Hierarchical Deep Network for Group Discovery and Multi-level Activity Recognition 11

Hierarchical Deep Network for Group Discovery and Multi-leve...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Goyal, Ashish Bhargava, Neha Chaudhuri, Subhasis Velmurugan, Rajbabu Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9781450366151

We present a deep network based hierarchical framework to recognize activities performed by people collectively from videos at various levels of granularity - individual person, group and overall (or scene level). Individual person analysis, which includes person detection, tracking, pose estimation and individual activity recognition, has been studied extensively. Most of the existing work on collective activity recognition has focused on overall scene activity estimation. However, in scenarios where multiple groups perform different group activities, overall scene activity recognition in isolation paints an incomplete picture of various activities. Identifying groups and recognizing their activities is therefore important to understand a scene in it's completeness. To this end, we add an extra layer in existing methods that finds the groups (or clusters) of people present in a scene and their activities. We then utilize these group activities along with the scene context to recognize the scene activity. To discover these groups, we propose a min-max criteria within the framework to train a sub-network which learns pairwise similarity between any two individuals, used by a clustering algorithm for identification of groups. The group activity is captured by an LSTM module whereas the individual and scene activities are captured by CNN-LSTM based modules. These modules along with the grouping layer form the proposed network. We evaluate the network on a publicly available dataset to indicate the usefulness of our approach.

关键词： Clustering algorithms

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Unsupervised Detection of Surface Mine Sites using Sentinel Multi-spectral imagery and Dual-polarimetric SAR Data 11

Unsupervised Detection of Surface Mine Sites using Sentinel ...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Aswatha, Shashaank M. Saini, Vishnu Mukherjee, Jayanta Biswas, Prabir K. Aikat, Subhas Misra, Arundhati Indian Inst Technol Kharagpur Kharagpur W Bengal India Indian Space Res Org Space Applicat Ctr Ahmadabad Gujarat India

ISBN: (纸本)9781450366151

Detecting surface mine activities has been one of the major challenges of remote sensing community to monitor ecological balance in the vicinity of mine area. Towards addressing this issue, we propose an unsupervised mine-site detection technique using multi-modal remotely sensed data from Sentinel missions. We use Sentinel-1's dual polarized SAR data, and Sentinel-2's multi-spectral data for this purpose. An initial set of seed pixels is sampled using spectral slopes based rules on multi-spectral images, which are tailored to capture mining area pixels that exhibit common characteristics. Then, we generate features using multi-spectral image indices and dual polarized SAR data Stokes' parameters. These features over locations sampled by slope based rules are used to train a one-class support vector machine to detect the land anomalies (mine activities). As observed from the experiments, multi-spectral features are efficient in separating mine region and built-up region from the rest of the land surface, and SAR image features discriminate built-up region from the rest of the image. We fuse both of these imagery features to encash their advantage in segregating mine sites in the considered study area. We demonstrate the efficiency of the proposed technique in detecting active coal mines over two study sites, Asansol region (in West bengal, India) and Jharia region (in Jharkhand, India), both of which exhibit common geographical characteristics. The average detection accuracy in our results is found to be around 94.8%.

关键词： Multi-spectral imagery synthetic aperture radar surface mining area detection multi-modal analysis spectral slopes

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