Visual Object Categorization via Sparse Representation

Fu, Huanzhang; Zhu, Chao; Dellandréa, Emmanuel; Bichot, Charles‐Edmond; Chen, Liming

doi:10.1109/icig.2009.100

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…Categorization allows for the organization of stimuli, ideas and events into meaningful “chunks.” 1‐4 Organizing information in such a way makes encoding and representing stimuli more efficient, as only information relevant to the category is necessary, and allows for immediate generalization to novel situations 5‐7 . Learning a new category involves multiple cognitive functions, including attention, working memory, long‐term memory and decision making, and therefore, requires complex interactions among multiple brain regions 8‐15 …”

Section: Introductionmentioning

confidence: 99%

Category learning in rodents using touchscreen‐based tasks

Broschard

Kim

Love

et al. 2020

Genes Brain and Behavior

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Categorization is a fundamental cognitive function that organizes our experiences into meaningful “chunks.” This category knowledge can then be generalized to novel stimuli and situations. Multiple clinical populations, including people with Parkinson's disease, amnesia, autism, ADHD and schizophrenia, have impairments in the acquisition and use of categories. Although rodent research is well suited for examining the neural mechanisms underlying cognitive functions, many rodent cognitive tasks have limited translational value. To bridge this gap, we use touchscreens to permit greater flexibility in stimulus presentation and task design, track key dependent measures, and minimize experimenter involvement. Touchscreens offer a valuable tool for creating rodent cognitive tasks that are directly comparable to tasks used with humans. Touchscreen tasks are also readily used with cutting‐edge neuroscientific methods that are difficult to do in humans such as optogenetics, chemogenetics, neurophysiology and calcium imaging (using miniscopes). In this review, we show advantages of touchscreen‐based tasks for studying category learning in rats. We also address multiple factors for consideration when designing category learning tasks, including the limitations of the rodent visual system, experimental design, and analysis strategies.

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Section: Introductionmentioning

confidence: 99%

Category learning in rodents using touchscreen‐based tasks

Broschard

Kim

Love

et al. 2020

Genes Brain and Behavior

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“…ridgelet, curvelet and contourlet, have been widely used in signal processing. Another possibility consists in using the dictionary composed by the training images themselves, which has also given promising results as in [31] and [8].…”

Section: Sparse Representation Modelmentioning

confidence: 99%

Reconstructive and Discriminative Sparse Representation for Visual Object Categorization

Fu¹,

Dellandréa²,

Chen³

2011

Procedings of the British Machine Vision Conference 2011

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Sparse representation was originally used in signal processing as a powerful tool for acquiring, representing and compressing high-dimensional signals. Recently, motivated by the great successes it has achieved, it has become a hot research topic in the domain of computer vision and pattern recognition. In this paper, we propose to adapt sparse representation to the problem of Visual Object Categorization which aims at predicting whether at least one or several objects of some given categories are present in an image. Thus, we have elaborated a reconstructive and discriminative sparse representation of images, which incorporates a discriminative term, such as Fisher discriminative measure or the output of a SVM classifier, into the standard sparse representation objective function in order to learn a reconstructive and discriminative dictionary.Experiments carried out on the SIMPLIcity image dataset have clearly revealed that our reconstructive and discriminative approach has gained an obvious improvement of the classification accuracy compared to standard SVM using image features as input. Moreover, the results have shown that our approach is more efficient than a sparse representation being only reconstructive, which indicates that adding a discriminative term for constructing the sparse representation is more suitable for the categorization purpose.

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Research of Recognition Algorithms by a Distance Feature Information Method on Digital Image

Wang

Huang

2013

AMM

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t be analysed about the recognition methods for digital image in this paper. In the digital image preprocessing, the conservative smoothing, mean filtering, Gaussian sharpening and binarization are used to improve the effectiveness of the digital feather extraction. A distance feature information method for recognizing digital images be proposed in this paper.The method be evaluated and compared by experimental tests.

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Visual Object Categorization via Sparse Representation

Cited by 3 publications

References 43 publications

Category learning in rodents using touchscreen‐based tasks

Category learning in rodents using touchscreen‐based tasks

Reconstructive and Discriminative Sparse Representation for Visual Object Categorization

Research of Recognition Algorithms by a Distance Feature Information Method on Digital Image

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