Limits...
Animal detection in natural images: effects of color and image database.

Zhu W, Drewes J, Gegenfurtner KR - PLoS ONE (2013)

Bottom Line: The ERP results of go/nogo and forced choice tasks were similar to those reported earlier.This result indicates ultra-fast processing of animal images is possible irrespective of the particular database.Therefore, we conclude that the ANID image database is better suited for the investigation of the processing of natural scenes than other databases commonly used.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science, Yunnan University, Kunming, China ; Department of Psychology, Giessen University, Giessen, Germany ; Kunming Institute of Zoology, Chinese Academy of Science, Kunming, China.

ABSTRACT
The visual system has a remarkable ability to extract categorical information from complex natural scenes. In order to elucidate the role of low-level image features for the recognition of objects in natural scenes, we recorded saccadic eye movements and event-related potentials (ERPs) in two experiments, in which human subjects had to detect animals in previously unseen natural images. We used a new natural image database (ANID) that is free of some of the potential artifacts that have plagued the widely used COREL images. Color and grayscale images picked from the ANID and COREL databases were used. In all experiments, color images induced a greater N1 EEG component at earlier time points than grayscale images. We suggest that this influence of color in animal detection may be masked by later processes when measuring reation times. The ERP results of go/nogo and forced choice tasks were similar to those reported earlier. The non-animal stimuli induced bigger N1 than animal stimuli both in the COREL and ANID databases. This result indicates ultra-fast processing of animal images is possible irrespective of the particular database. With the ANID images, the difference between color and grayscale images is more pronounced than with the COREL images. The earlier use of the COREL images might have led to an underestimation of the contribution of color. Therefore, we conclude that the ANID image database is better suited for the investigation of the processing of natural scenes than other databases commonly used.

Show MeSH
Grand average ERP waveforms of experiment 1.Frontal areas: F3, F4, F7, F8, FZ, FP1, FP2. ERP waveforms elicited by Color and Gray-scale images and their difference waveforms in COREL (A) and ANID (B) databases separately. Topographic maps for the difference waves in COREL (C) and (D). The ERPs were integrated across 20 ms time windows from 120 ms to 259 ms. Maps are viewed from above, with the nose pointing upwards. (F) ERP waveforms of COREL and ANID and their difference waveforms at frontal areas. (E) and (G) Statistics of N1 amplitudes (left) and latency (right). Error bars represent 1 s.e.m.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3794973&req=5

pone-0075816-g005: Grand average ERP waveforms of experiment 1.Frontal areas: F3, F4, F7, F8, FZ, FP1, FP2. ERP waveforms elicited by Color and Gray-scale images and their difference waveforms in COREL (A) and ANID (B) databases separately. Topographic maps for the difference waves in COREL (C) and (D). The ERPs were integrated across 20 ms time windows from 120 ms to 259 ms. Maps are viewed from above, with the nose pointing upwards. (F) ERP waveforms of COREL and ANID and their difference waveforms at frontal areas. (E) and (G) Statistics of N1 amplitudes (left) and latency (right). Error bars represent 1 s.e.m.


Animal detection in natural images: effects of color and image database.

Zhu W, Drewes J, Gegenfurtner KR - PLoS ONE (2013)

Grand average ERP waveforms of experiment 1.Frontal areas: F3, F4, F7, F8, FZ, FP1, FP2. ERP waveforms elicited by Color and Gray-scale images and their difference waveforms in COREL (A) and ANID (B) databases separately. Topographic maps for the difference waves in COREL (C) and (D). The ERPs were integrated across 20 ms time windows from 120 ms to 259 ms. Maps are viewed from above, with the nose pointing upwards. (F) ERP waveforms of COREL and ANID and their difference waveforms at frontal areas. (E) and (G) Statistics of N1 amplitudes (left) and latency (right). Error bars represent 1 s.e.m.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3794973&req=5

pone-0075816-g005: Grand average ERP waveforms of experiment 1.Frontal areas: F3, F4, F7, F8, FZ, FP1, FP2. ERP waveforms elicited by Color and Gray-scale images and their difference waveforms in COREL (A) and ANID (B) databases separately. Topographic maps for the difference waves in COREL (C) and (D). The ERPs were integrated across 20 ms time windows from 120 ms to 259 ms. Maps are viewed from above, with the nose pointing upwards. (F) ERP waveforms of COREL and ANID and their difference waveforms at frontal areas. (E) and (G) Statistics of N1 amplitudes (left) and latency (right). Error bars represent 1 s.e.m.
Bottom Line: The ERP results of go/nogo and forced choice tasks were similar to those reported earlier.This result indicates ultra-fast processing of animal images is possible irrespective of the particular database.Therefore, we conclude that the ANID image database is better suited for the investigation of the processing of natural scenes than other databases commonly used.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science, Yunnan University, Kunming, China ; Department of Psychology, Giessen University, Giessen, Germany ; Kunming Institute of Zoology, Chinese Academy of Science, Kunming, China.

ABSTRACT
The visual system has a remarkable ability to extract categorical information from complex natural scenes. In order to elucidate the role of low-level image features for the recognition of objects in natural scenes, we recorded saccadic eye movements and event-related potentials (ERPs) in two experiments, in which human subjects had to detect animals in previously unseen natural images. We used a new natural image database (ANID) that is free of some of the potential artifacts that have plagued the widely used COREL images. Color and grayscale images picked from the ANID and COREL databases were used. In all experiments, color images induced a greater N1 EEG component at earlier time points than grayscale images. We suggest that this influence of color in animal detection may be masked by later processes when measuring reation times. The ERP results of go/nogo and forced choice tasks were similar to those reported earlier. The non-animal stimuli induced bigger N1 than animal stimuli both in the COREL and ANID databases. This result indicates ultra-fast processing of animal images is possible irrespective of the particular database. With the ANID images, the difference between color and grayscale images is more pronounced than with the COREL images. The earlier use of the COREL images might have led to an underestimation of the contribution of color. Therefore, we conclude that the ANID image database is better suited for the investigation of the processing of natural scenes than other databases commonly used.

Show MeSH