23 research outputs found
Stochasticity: A Feature for the Structuring of Large and Heterogeneous Image Databases
International audienceThe paper addresses image feature characterization and the structuring of large and heterogeneous image databases through the stochasticity or randomness appearance. Measuring stochasticity involves finding suitable representations that can significantly reduce statistical dependencies of any order. Wavelet packet representations provide such a framework for a large class of stochastic processes through an appropriate dictionary of parametric models. From this dictionary and the Kolmogorov stochasticity index, the paper proposes semantic stochasticity templates upon wavelet packet sub-bands in order to provide high level classification and content-based image retrieval. The approach is shown to be relevant for texture images
Multi-Layer Local Graph Words for Object Recognition
In this paper, we propose a new multi-layer structural approach for the task
of object based image retrieval. In our work we tackle the problem of
structural organization of local features. The structural features we propose
are nested multi-layered local graphs built upon sets of SURF feature points
with Delaunay triangulation. A Bag-of-Visual-Words (BoVW) framework is applied
on these graphs, giving birth to a Bag-of-Graph-Words representation. The
multi-layer nature of the descriptors consists in scaling from trivial Delaunay
graphs - isolated feature points - by increasing the number of nodes layer by
layer up to graphs with maximal number of nodes. For each layer of graphs its
own visual dictionary is built. The experiments conducted on the SIVAL and
Caltech-101 data sets reveal that the graph features at different layers
exhibit complementary performances on the same content and perform better than
baseline BoVW approach. The combination of all existing layers, yields
significant improvement of the object recognition performance compared to
single level approaches.Comment: International Conference on MultiMedia Modeling, Klagenfurt :
Autriche (2012
Human Daily Activities Indexing in Videos from Wearable Cameras for Monitoring of Patients with Dementia Diseases
Our research focuses on analysing human activities according to a known
behaviorist scenario, in case of noisy and high dimensional collected data. The
data come from the monitoring of patients with dementia diseases by wearable
cameras. We define a structural model of video recordings based on a Hidden
Markov Model. New spatio-temporal features, color features and localization
features are proposed as observations. First results in recognition of
activities are promising
Towards Automatic Honey Bee Flower-Patch Assays with Paint Marking Re-Identification
In this paper, we show that paint markings are a feasible approach to
automatize the analysis of behavioral assays involving honey bees in the field
where marking has to be as lightweight as possible. We contribute a novel
dataset for bees re-identification with paint-markings with 4392 images and 27
identities. Contrastive learning with a ResNet backbone and triplet loss led to
identity representation features with almost perfect recognition in closed
setting where identities are known in advance. Diverse experiments evaluate the
capability to generalize to separate IDs, and show the impact of using
different body parts for identification, such as using the unmarked abdomen
only. In addition, we show the potential to fully automate the visit detection
and provide preliminary results of compute time for future real-time deployment
in the field on an edge device.Comment: Paper 17, workshop "CV4Animals: Computer Vision for Animal Behavior
Tracking and Modeling", in conjunction with Computer Vision and Pattern
Recognition (CVPR 2023), June 18, 2023, Vancouver, Canad
The IMMED Project: Wearable Video Monitoring of People with Age Dementia
International audienceIn this paper, we describe a new application for multimedia indexing, using a system that monitors the instrumental activities of daily living to assess the cognitive decline caused by dementia. The system is composed of a wearable camera device designed to capture audio and video data of the instrumental activities of a patient, which is leveraged with multimedia indexing techniques in order to allow medical specialists to analyze several hour long observation shots efficiently
Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia
International audienceThis paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients' houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach
Multiple Feature Fusion Based on Co-Training Approach and Time Regularization for Place Classification in Wearable Video
The analysis of video acquired with a wearable camera is a challenge that multimedia community is facing with the proliferation of such sensors in various applications. In this paper, we focus on the problem of automatic visual place recognition in a weakly constrained environment, targeting the indexing of video streams by topological place recognition. We propose to combine several machine learning approaches in a time regularized framework for image-based place recognition indoors. The framework combines the power of multiple visual cues and integrates the temporal continuity information of video. We extend it with computationally efficient semisupervised method leveraging unlabeled video sequences for an improved indexing performance. The proposed approach was applied on challenging video corpora. Experiments on a public and a real-world video sequence databases show the gain brought by the different stages of the method
Detection and Compensation of Landmark Errors in Monte Carlo Localization
International audienceThis paper studies a new Monte Carlo vision-based localization algorithm which performs on-line detection and compensation of measurement biases. For that purpose, the state vector is augmented to include the mobile coordinates and orientation, but also discrete latent variables indicating the validity of each landmark angular measurement. An appropriate particle filter is then proposed to solve the resulting non-linear filtering problem. The efficiency of this filter is guarantied by using relevant models for the different kinds of systematic errors corrupting the angular measurements. Simulation results illustrate the gain of the proposed approach when compared to a more conventional method