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Content-Based Image Retrieval (CBIR) is a broad research field in the current digital world. This paper focuses on content-based image retrieval based on visual properties, consisting of high-level semantic information. The variation between low-level and high-level features is identified as a semantic gap. The semantic gap is the biggest problem in CBIR. The visual characteristics are extracted from low-level features such as color, texture and shape. The low-level feature increases CBIRs performance level. The paper mainly focuses on an image retrieval system called combined color (TriCLR) (RGB, YCbCr, and $L^{\ast }{a}^{\ast }{b}^{\ast }$) with the histogram of texture features in LBP (HistLBP), which, is known as a hybrid of three colors (TriCLR) with Histogram of LBP (TriCLR and HistLBP). The study also discusses the hybrid method in light of low-level features. Finally, the hybrid approach uses the (TriCLR and HistLBP) algorithm, which provides a new solution to the CBIR system that is better than the existing methods.

Polycystic Ovary Syndrome (PCOS) is a metabolic reproductive disorder characterized condition by an extended menstrual cycle. There are many methods currently in use, but they all have major limitations. The prediction rate, which takes longer due to factors like heterogeneity is one of the main aspects of PCOS that makes it difficult. Moreover, there was no correlation between the network’s generalization ability assessment and precise predictions. The ELMAN Neural Network has been used to identify PCOS in order to eliminate the aforementioned problems. The ovarian ultrasound image is pre-processed with Fast Local Laplacian Filter (FLLF) and Brightness Preserving Bi-Histogram Equalization. The Contextual Ensemble Network (CENET) is used in the segmentation process and the textural features are extracted using the Projective Integral (PI) and the color features are extracted using the Color Auto Correlogram (CAC). Finally, an Elman Network with a Green Anaconda Optimization (GAO) is employed for classification purposes to diagnose PCOS. According to the results of the experimental research, the proposed ELMAN network has an accuracy of 95%, 93% for precision, 92.5% for recall, 90% for specificity, F1-score is 91%. Thus, the CENET with ELMAN Neural Network for PCOS detection from ultrasound images was considerably simpler and more efficient.

In the last few decades, crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems. While human detection in partially crowded scenarios have achieved many reliable works, a highly dense crowd-like situation still is far from being solved. Densely crowded scenes offer patterns that could be used to tackle these challenges. This problem is challenging due to the crowd volume, occlusions, clutter and distortion. Crowd region classification is a precursor to several types of applications. In this paper, we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel dissimilarity. Experimental results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.