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    A Novel Color Difference-Based Method for Palette Extraction and Evaluation Using Images of Birds
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Kosesoy, Melike Bektas; Yilmaz, Seckin
    Color palettes are important sources of inspiration for designers. In most coloring studies, designers and interior architects use ready-made color palettes that are known to be harmonious and popular with clients. In this study, a new color palette extraction and evaluation method is proposed that differs from existing methods because it accelerates designers' product coloring processes by identifying the color-area and color-neighborhood relationship. Furthermore, attractive color palettes will be identified by focusing on living creatures, primarily colorful birds. For this purpose, a new data set of images of birds was created. In the literature, the success of color palettes is usually evaluated through questionnaires, while measurements are made without relying on numerical metrics. In this study and, for the first time, a new metric based on CIEDE2000 and the Hungarian algorithm to measure the accuracy of color palettes is demonstrated. In the proposed method birds are first segmented from background using Mask R-CNN and PointRend models. Then, Fuzzy c-means (FCM), Gaussian Mixture Model (GMM), k-means, Mean Shift, and mini batch k-means algorithms were used to extract the color palette of each bird image. Color reduction based on the CIEDE2000 was performed on the color palettes using various threshold values, resulting in the final color palettes. In the last step, color-area and color-neighborhood relationships are shown through graph-based color palettes. Experimental results showed that the PointRend method, with a 93.92% success rate, produced the most successful results. However, k-means and GMM methods were more successful in extracting the colors of birds. The evaluation of the palettes was performed with the proposed numerical metric rather than questionnaires, resulting in more robust and repeatable findings. Furthermore, the proposed graph-based color palette results in considerably simplified coloring process by reducing the computational complexity for an industrial product.
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    Comprehensive survey of the solving puzzle problems
    (Elsevier, 2023) Yilmaz, Seckin; Nabiyev, Vasif V.
    Solving puzzle problems using computer-aided methods is becoming more common with applications in forensic science, restoration, banking system, and multimedia. However, only a few surveys have been published on this topic, the most recent being more than a decade old. The scope of 2D puzzle problems is extensive, and the number of computer-aided methods has increased in recent years. In this paper, we have presented a comprehensive survey to pave a roadmap for researchers dealing with puzzle problems. This study classifies 2D puzzle problems in a novel way, considering many examples such as dissection, combinatorial and double-sided puzzles and reclassifies computer-aided methods to cover the studies carried out in recent years. Various strategies (pre-grouping and global consistency approach) have been investigated to solve the puzzle problem effectively. The computer-aided methods have been examined deeply, including many recent methods related to squared jigsaw puzzles, torn photographs, banknotes, and fragmented documents, and they are compared to each other. In addition, new topics such as combining mosaic pieces and Islamic architectural motif puzzle problems have been proposed to the interest of researchers. In conclusion, our study shows many research opportunities that are not yet solved by any computer-aided method. (c) 2023 Elsevier Inc. All rights reserved.
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    QueryTrack: identifying and tracking a person of interest using clothing-based hybrid features
    (Springer, 2026) Ortac kosun, Gizem; Yilmaz, Seckin; Samli, Ruya
    Locating and tracking a specific person of interest in a single visual query remains a significant challenge in complex surveillance environments. Current paradigms fall short: generic multi-object trackers suffer from identity loss over time, while existing person search methods, designed for static image galleries, lack robustness against the dynamic complexities of video streams, especially occlusions. This paper introduces QueryTrack, a comprehensive framework designed specifically for this query-based tracking task. The core novelty lies in a powerful re-identification engine that fuses four distinct feature types-HOG, Gabor, Color, and VGG16-into a highly discriminative signature for the target. This signature drives a hybrid tracking algorithm that synergizes motion prediction and visual tracking to maintain identity continuity. Furthermore, we propose a new post-occlusion recovery technique to handle long-term disappearances. Experimental evaluations validate our method's superior performance, achieving F1 scores of 97.20%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$97.20\%$$\end{document} in crowded scenarios and 96.35%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$96.35\%$$\end{document} with minimal occlusion, confirming its significant contribution to accurate and persistent person tracking under realistic conditions. Additionally, we provide a transparent computational cost analysis, confirming the system's viability for offline forensic investigation where accuracy is paramount.