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Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

This study delves into the impact of mindfulness on the retention intention of technology employees, with a particular focus on the mediating variables of affective commitment (AC) and organizational identification (OI). The primary aim is to gain a comprehensive understanding of the underlying mechanisms through which mindfulness influences the retention intention of technology employees.

The research employed a survey approach with self-administered questionnaires and structural equation modeling. The collected data were analyzed using Statistical Product and Service Solutions (SPSS) 24 and Analysis of Moment Structure (AMOS) 28. Multiple mediation analyses was conducted through AMOS to examine the mediating effects of OI and AC.

The association between mindfulness and retention intention among technology employees showed an overall positive correlation. Additionally, AC and OI were positively correlated with retention intention. In the impact of employee mindfulness (EM) on retention intention, all indirect effects were found to be significant.

To the best of the authors' knowledge, this study is the first to investigate the relationship between EM and retention intention, as well as the associations of AC and OI with them, extending the application of mindfulness in management and offering insights for talent retention among company decision-makers.