Visual search for objects in a complex visual context: what we wish to see
type: Dataset
description: In this work we propose a saliency based psycho-visual weighting of the BoVW for object recognition. This approach is designed to identify objects related to IADL on videos recorded by a wearable camera. These recording give an egocentric point-of-view on the upcoming action. This point- of-view is also characterized by a complex visual scene with several objects on the frame plan. The human visual system functions is a way to process only the relevant data by considering areas of interest. Based on this idea, we propose a new approach by introducing saliency models to discard irrelevant information in the video frames. Therefore we apply a visual saliency model to weight the image signature within the BoVW framework. Visual saliency is well suited for catching spatio-temporal information related to the observer's attention on the video frame. We also proposed an additional geometric saliency cue that models the anticipation phenomenon observed on subjects watching video content from the wearable camera. The findings show that discarding irrelevant features gives better performances when compared to the baseline method which consider the whole set of features in the images.
issued: 2026-05-05T10:49:40.745850
language: en
modified: 2026-05-05T10:49:40.745855
contact point: Organization
  fn: CCSD
keyword: book-sections
keyword: infoeu-reposemanticsbookpart
keyword: infoinfo-tscomputer-science-cssignal-and-image-processing
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
landing page: Semantic Multimedia Analysis and Processing

Visual search for objects in a complex visual context: what we wish to see
type: Distribution
format: HTML
issued: 2026-05-05T10:49:40.749036
modified: 2026-05-05T10:49:40.738498
access URL: https://hal.science/hal-00993264

test_moissonnage_selune
type: Agent