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Emotion based music player is an interdisciplinary study of computer vision and psychology. As music enhances the positive vibes it plays a significant role in soothing people's emotion. Emotions can be predicted through facial expression analysis using vision-based methods. However, challenges like environment and expression complexity have become hindrance to attain a good recognition rate. Therefore, we put forward a deep dual domain joint feature framework based on linear discriminant analysis for facial emotion recognition. First, we detect the human face and learn the emotion pattern using the popular complementary deep domain networks called EfficientNet and ResNet50. The learned deep dual domain space is projected onto linear discriminant space to achieve a joint discriminant feature space. The recognition rate of the proposed joint discriminant feature framework is analyzed using support vector machine. To prove the efficacy of the proposed framework, we validated it on two Benchmarks namely FER2013 and CK48+ datasets. The proposed framework achieved a good recognition rate of 99% and 98.6% on FER2013 and CK48+ respectively. Experimental analysis on our EmDe dataset showed an accuracy of 99% and proves that the deep dual domain joint discriminant framework as a promising pipeline for emotion-based music player system. [ABSTRACT FROM AUTHOR]

Kolkata, renowned as the City of Joy, boasts a rich tapestry of cultural heritage spanning centuries. Despite the significance of its architectural marvels, accessing comprehensive visual documentation of Kolkata's heritage sites remains a challenge. In online searches, limited imagery often fails to provide a detailed understanding of these historical landmarks. To address this gap, this paper introduces MonuNet, a high-performance deep-learning network specifically designed for the classification of heritage images from Kolkata. The development of MonuNet addresses the critical need for efficient and accurate identification of Kolkata's architectural marvels, which are significant tangible cultural heritages. The dataset used to train MonuNet is organized by heritage sites, each category within the dataset represents distinct sites. It includes images from 13 prominent heritage sites in Kolkata. For each of these sites, there are 50 images, making it a structured collection where each category (heritage site) is equally represented. The proposed network utilizes a unique architecture incorporating a Dense channel attention module and a Parallel-spatial channel attention module to capture intricate architectural details and spatial relationships within the images. Experimental evaluations demonstrate the superior performance of MonuNet in classifying Kolkata heritage images with an accuracy of 89%, Precision of 87.77%, and Recall of 86.61%. The successful deployment of MonuNet holds significant implications for cultural preservation, tourism enhancement, and urban planning in Kolkata, aligning with the United Nations Sustainable Development Goals (SDGs) for sustainable city development. By providing a robust tool for the automatic identification and classification of heritage images, MonuNet promises to enrich online repositories with detailed visual documentation, thereby enhancing accessibility to Kolkata's cultural heritage for researchers, tourists, and urban planners alike. [ABSTRACT FROM AUTHOR]

In India, kolam, commonly referred to as rangoli, is a traditional style of art. It involves using rice flour, chalk, or coloured powders to create elaborate patterns and motifs on the ground. Kolam is a common daily ritual in many regions of India, especially in South India, where it is seen as a significant cultural tradition and a means to greet visitors. Unfortunately, as a result of people’s hectic lives nowadays, the habit of drawing kolam on a regular basis is dwindling. The art of making kolam patterns is in danger of disappearing as so many individuals no longer have the time or space to do it on a regular basis. Therefore, it is imperative that ancient art be conserved and digitally documented in order to enlighten our next generation about kolam and its classifications. Deep learning has become a powerful technique because of its ability to learn from raw image data without the aid of a feature engineering process. In this article, we attempted to understand the types of Kolam images using the proposed deep architecture called KolamNetV2. KolamNetV2 comprises EfficientNet and attention layers, ensuring high accuracy with minimal training data and parameters. We evaluated KolamNetV2 to reveal its ability to learn the various types in our challenging Kolam dataset. The experimental findings show that the proposed network achieves fine enhancement in performance metrics viz, precision-0.7954, recall-0.7846, F1score-0.7854 and accuracy-81%. We compared our results with state-of-the-art deep learning methodologies, proving the astounding capability. [ABSTRACT FROM AUTHOR]

Video shot boundary detection (VSBD) is the fundamental step for video processing algorithms. The goal of any VSBD algorithm is to detect the transitions (abrupt or subtle) in the given video precisely. In this paper, a visual significance model that is suitable for describing transitions in video is introduced. The proposed visual significance model is composed of parameters like color, texture, edge, motion and focus computed over the frames in the video. Once the visually significant region is identified in each frame of the video, the temporal signature is generated through the dissimilarity measure of the visual significance model. The temporal signature is further examined using standard Random Vector Functional Link (RVFL) networks for categorizing the transitions as Abrupt Transitions (AT), Subtle Transitions (ST) and No Transitions (NT). To validate the performance of the proposed visual significance model based VSBD Framework, it is evaluated on benchmarks like VIDEOSEG2004 and TRECVID2001 to detect and categorize the transitions. Comparison of F1-Score measure with prominent early methods reveals that the proposed framework is a promising model for detecting the transitions in videos even in the presence of varying illumination conditions, fast camera and object motion. [ABSTRACT FROM AUTHOR]