Salient region detection and object segmentation in color images using dynamic mode decomposition

Sikha, O. K.; Kumar, Shailendra; Soman, K P

doi:10.1016/j.jocs.2017.07.007

Cited by 32 publications

(11 citation statements)

References 21 publications

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“…Importantly, the consideration of aggregated and summary features does not give insights into the underlying reasons for these differences, such as filter use, differences in image content, and even differences in the intent and motivations behind the use of these platforms for image sharing. Our future work aims to explore color composition in different regions of the image, e.g., comparing background and foreground colors or colors of salient regions (60) to identify deeper signals of image differences.…”

Section: Characteristics Of Image Posts On Instagrammentioning

confidence: 99%

Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders

et al. 2021

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Background and Objectives: Prior research has successfully identified linguistic and behavioral patterns associated with schizophrenia spectrum disorders (SSD) from user generated social media activity. Few studies, however, have explored the potential for image analysis to inform psychiatric care for individuals with SSD. Given the popularity of image-based platforms, such as Instagram, investigating user generated image data could further strengthen associations between social media activity and behavioral health.Methods: We collected 11,947 Instagram posts across 68 participants (mean age = 23.6; 59% male) with schizophrenia spectrum disorders (SSD; n = 34) and healthy volunteers (HV; n = 34). We extracted image features including color composition, aspect ratio, and number of faces depicted. Additionally, we considered social connections and behavioral features. We explored differences in usage patterns between SSD and HV participants.Results: Individuals with SSD posted images with lower saturation (p = 0.033) and lower colorfulness (p = 0.005) compared to HVs, as well as images showing fewer faces on average (SSD = 1.5, HV = 2.4, p < 0.001). Further, individuals with SSD demonstrated a lower ratio of followers to following compared to HV participants (p = 0.025).Conclusion: Differences in uploaded images and user activity on Instagram were identified in individuals with SSD. These differences highlight potential digital biomarkers of SSD from Instagram data.

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Section: Characteristics Of Image Posts On Instagrammentioning

confidence: 99%

Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders

et al. 2021

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“…The successfully identified non-salient pixels are not considered either by precision or recall. This affects the methods that correctly identified non-salient pixels but failed to correctly detect salient pixels [146,147]. In this study, MAE between saliency map and ground truth was also computed for a balanced evaluation to take this effect into account.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph

Olugbara

2021

J. Imaging

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Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.

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“…It is a very powerful tool for analysing the dynamics of non-linear systems and was developed by Schmid [35]. It is also used for forecasting [49], natural language processing [50], salient region detection from images [38], etc. It was inspired by and closely related to Koopman-operator analysis [36].…”

Section: B Dynamic Mode Decomposition (Dmd)mentioning

confidence: 99%

Dynamic Mode Decomposition based feature for Image Classification

Rahul-Vigneswaran

Sachin-Kumar

Mohan

2019

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)

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Irrespective of the fact that Machine learning has produced groundbreaking results, it demands an enormous amount of data in order to perform so. Even though data production has been in its all-time high, almost all the data is unlabelled, hence making them unsuitable for training the algorithms. This paper proposes a novel method 1 of extracting the features using Dynamic Mode Decomposition (DMD). The experiment is performed using data samples from Imagenet. The learning is done using SVM-linear, SVM-RBF, Random Kitchen Sink approach (RKS). The results have shown that DMD features with RKS give competing results.

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Salient region detection and object segmentation in color images using dynamic mode decomposition

Cited by 32 publications

References 21 publications

Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders

Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Dynamic Mode Decomposition based feature for Image Classification

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