Automatic Recognition of Human Emotions Induced by Visual Contents of Digital Images Based on Color Histogram

Mohseni, Seyed Abdolreza; Wu, Hong Ren; Thom, James A.

doi:10.1109/dicta.2017.8227410

Cited by 3 publications

(14 citation statements)

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“…The relationship VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ between the human emotions and visual contents of images has been investigated by different researchers [1], [3], [4], [6]. The research work has mainly focused on features of images such as color, texture, and shapes, as well as how these features affect human emotions.…”

Section: A Revc Literature Reviewmentioning

confidence: 99%

“…Color histograms have been used in different applications, including REVC systems [1], image retrieval [6], [13], [14], [15], [16], [17] image indexing [18], image clustering [19], object tracking [20], [21], [22], video conversion [23], segmentation [24], object recognition [25], topological maps [26], particle detection [27], pedestrian tracking in videos [28], and appearance-based person reidentification [29], [30].…”

Section: B Color Histogram Literature Reviewmentioning

confidence: 99%

“…Color histograms have been rarely used in REVC [1], [3] and in an emotion image retrieval system [6], which is closely related to REVC. For instance, a color histogram was used for a color emotion image retrieval system as reported by Solli and Lenz where 8 bins were chosen [6].…”

Section: B Color Histogram Literature Reviewmentioning

confidence: 99%

“…Color histograms have been rarely used by researchers in connection with REVC systems, and when they were used, there was no or little investigation about the effect of using different numbers of color histogram bins in reported experiments (e.g., [3], [6]). To the authors' best knowledge, only Mohseni et al [1] investigated the performance of a color histogram-based REVC system based on overall performance of classifiers [1], but not for individual emotion categories. Color histogram was examined in both HSV and RGB color spaces and it was shown that 8 bins is the optimum number of bins in both RGB and HSV color spaces based on the overall performance of classifiers [1].…”

Section: B Color Histogram Literature Reviewmentioning

confidence: 99%

“…Because there are many images to be searched, automated retrieval systems are highly valued. One aspect of automated image retrieval is Recognition of human Emotions induced by the Visual Contents (REVC) [1]. REVC systems can be advantageous to various content indexing and retrieval applications [2] when looking for images based on their emotional contents, but not facial.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Recognizing Induced Emotions With Only One Feature: A Novel Color Histogram-Based System

Mohseni

Thom

et al. 2020

IEEE Access

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Emotions can be evoked in humans by images. Previous reports on Recognition of Emotions induced by Visual Content of images (REVC) mainly focused on numerous features to improve recognition performance. To devise a more robust REVC system, this paper examines the performance of a wide range of classifiers using color histogram as a single feature. Different numbers of color histogram bins in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces are considered in the examination and the overall classification performance is compared across the bin sizes. This investigation shows that features are not the only important factors affecting the þperformance of REVC systems, but also the type of classifiers and their parameters. This study shows that the HSV color space is better suited than the RGB color space for REVC systems. þThis paper proposes a new optimization algorithm called Optimizing Parameters of Ensemble RUSboosted Tree (OPERT) to boost the performance of the REVC system. Furthermore, a novel REVC system called Color histogram with Optimized RUSboosted Tree (CORT) is introduced. It is shown that our method is simpler, faster, and more efficient than the state-of-the-art, while providing comparable recognition performance. The robustness of the CORT system is validated over three different image datasets.

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Section: A Revc Literature Reviewmentioning

confidence: 99%