Biased competition in visual processing hierarchies: a learning approach using multiple cues - Archive ouverte HAL Access content directly
Journal Articles Cognitive Computation Year : 2011

Biased competition in visual processing hierarchies: a learning approach using multiple cues

Abstract

In this contribution, we present a large-scale hierarchical system for object detection fusing bottom-up (signal-driven) processing results with top-down (model or task-driven) attentional modulation. Specifically, we focus on the question of how the autonomous learning of invariant models can be embedded into a performing system and how such models can be used to define object-specific attentional modulation signals. Our system implements bi-directional data flow in a processing hierarchy. The bottom-up data flow proceeds from a preprocessing level to the hypothesis level where object hypotheses created by exhaustive object detection algorithms are represented in a roughly retino- topic way. A competitive selection mechanism is used to determine the most confident hypotheses, which are used on the system level to train multimodal models that link object identity to invariant hypothesis properties. The top-down data flow originates at the system level, where the trained multimodal models are used to obtain space- and feature- based attentional modulation signals, providing biases for the competitive selection process at the hypothesis level. This results in object-specific hypothesis facilitation/sup- pression in certain image regions which we show to be applicable to different object detection mechanisms. In order to demonstrate the benefits of this approach, we apply the system to the detection of cars in a variety of chal- lenging traffic videos. Evaluating our approach on a pub- licly available dataset containing approximately 3,500 annotated video images from more than 1 h of driving, we can show strong increases in performance and generaliza- tion when compared to object detection in isolation. Furthermore, we compare our results to a late hypothesis rejection approach, showing that early coupling of top- down and bottom-up information is a favorable approach especially when processing resources are constrained.
Fichier principal
Vignette du fichier
COGN-106R2.pdf (1.35 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-00647809 , version 1 (16-12-2011)

Identifiers

  • HAL Id : hal-00647809 , version 1

Cite

Alexander Gepperth, Sven Rebhan, Stephan Hasler, Jannik Fritsch. Biased competition in visual processing hierarchies: a learning approach using multiple cues. Cognitive Computation, 2011, 3 (1). ⟨hal-00647809⟩
92 View
162 Download

Share

Gmail Mastodon Facebook X LinkedIn More