Feature repetition effects on object familiarity: Evidence from an old/new recognition task

2010-08-14
Eren, Selda
Hohenberger, Annette Edeltraud
We performed an old/new study/test recognition task to investigate feature repetition effects on object familiarity. The results showed that repeated features increased "old" responses during the test phase for new objects. This increase was linear with the number of repeated features on the object. Old objects, which had been among the study phase stimuli, were not affected by the number of repeated features on the object. We also analyzed the effect of feature type (colour, shape, border and pattern) on familiarity responses. We found an effect of feature type only for the old objects. Saliency of the features also affected familiarity: the more salient the repeated feature was, the more familiar the object was found. We propose that the feature repetition effect for the new objects might be due to (1) activation of more than one representation constructed during the study phase (2) a separate representation for the repeated features, which has the potential to interfere with several perceptual processes.
32nd Annual Meeting of the Cognitive-Science-Society

Suggestions

Fine-grained object recognition and zero-shot learning in multispectral imagery
Sumbul, Gencer; Cinbiş, Ramazan Gökberk; AKSOY, SELİM (2018-05-05)
We present a method for fine-grained object recognition problem, that aims to recognize the type of an object among a large number of sub-categories, and zero-shot learning scenario on multispectral images. In order to establish a relation between seen classes and new unseen classes, a compatibility function between image features extracted from a convolutional neural network and auxiliary information of classes is learnt. Knowledge transfer for unseen classes is carried out by maximizing this function. Per...
Representation Learning for Contextual Object and Region Detection in Remote Sensing
Firat, Orhan; Can, Gulcan; Yarman Vural, Fatoş Tunay (2014-08-28)
The performance of object recognition and classification on remote sensing imagery is highly dependent on the quality of extracted features, amount of labelled data and the priors defined for contextual models. In this study, we examine the representation learning opportunities for remote sensing. First we attacked localization of contextual cues for complex object detection using disentangling factors learnt from a small amount of labelled data. The complex object, which consists of several sub-parts is fu...
Object Detection with Convolutional Context Features
Kaya, Emre Can; Alatan, Abdullah Aydın (2017-01-01)
A novel extension to Huh B-ESA object detection algorithm is proposed in order to learn convolutional context features for determining boundaries of objects better. For input images, the hypothesis windows and their context around those windows are learned through convolutional layers as two parallel networks. The resulting object and context feature maps are combined in such a way that they preserve their spatial relationship. The proposed algorithm is trained and evaluated on PASCAL VOC 2007 detection ben...
Rescoring detections based on contextual scores in object detection
Zorlu, Ersan Vural; Akbaş, Emre; Department of Computer Engineering (2019)
To detect objects in an image, current state-of-the-art object detectors firstly definecandidate object locations, and then classify each of them into one of the predefinedcategories or as background. They do so by using the visual features extracted locallyfrom the candidate locations; omitting the rich contextual information embedded inthe whole image. Contextual information can be utilized to complement the informa-tion extracted locally and thereby to improve object detection accuracy. Researchershave p...
Visual object tracking using semi supervised convolutional filters
Sevindik, Emir Can; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2020-10-15)
Visual object tracking aims to find a single object position in a video frame, when a annotated bounding box is provided in the first frame. Correlation filters have always produced excellent results in terms of accuracy, while enjoying quite low computational complexity. The main property of correlation filter based trackers is to find a filter that can generate high values around the true target object location, whereas relatively low values for the locations away from the object. Recently, deep learn...
Citation Formats
S. Eren and A. E. Hohenberger, “Feature repetition effects on object familiarity: Evidence from an old/new recognition task,” presented at the 32nd Annual Meeting of the Cognitive-Science-Society, Portland, OR, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54995.