195,318 research outputs found

    Investigating the Behavior of Compact Composite Descriptors in Early Fusion, Late Fusion and Distributed Image Retrieval

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    In Content-Based Image Retrieval (CBIR) systems, the visual content of the images is mapped into a new space named the feature space. The features that are chosen must be discriminative and sufficient for the description of the objects. The key to attaining a successful retrieval system is to choose the right features that represent the images as unique as possible. A feature is a set of characteristics of the image, such as color, texture, and shape. In addition, a feature can be enriched with information about the spatial distribution of the characteristic that it describes. Evaluation of the performance of low-level features is usually done on homogenous benchmarking databases with a limited number of images. In real-world image retrieval systems, databases have a much larger scale and may be heterogeneous. This paper investigates the behavior of Compact Composite Descriptors (CCDs) on heterogeneous databases of a larger scale. Early and late fusion techniques are tested and their performance in distributed image retrieval is calculated. This study demonstrates that, even if it is not possible to overcome the semantic gap in image retrieval by feature similarity, it is still possible to increase the retrieval effectiveness

    Performance of the Progressive Wavelet Correlation for Image Retrieval

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    In the paper we present our experience with using progressive wavelet correlation for image retrieval. We perform a series of image search experiments that cover the following scenarios: (A) the given image is present in the database; (B) copies of the given image are present but with different names; (C) similar (but not identical) images are present; and (D) the given image is not present. Experiments are performed with data bases up to 1000 images, using the Oracle database and the Matlab component Database Toolbox for operations with databases

    Application of the Progressive Wavelet Correlation in Image Retrieving

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    In the paper we apply progressive wavelet correlation along with Fourier methods for searching and retrieving images stored in a database. The searching consists of three incremental steps, each of which quadruples the number of correlation points. The process can be halted at any stage if the intermediate results indicate that the correlation will not result in a match. We perform a series of image search experiments that cover the following scenarios: (A) the given image is present in the database; (B) copies of the given image are present but with different names; (C) similar (but not identical) images are present; and (D) the given image is not present. Experiments are performed with data bases up to 1000 images, using the Oracle database and the Matlab component Database Toolbox for operations with databases

    THE COST OF DIVERSITY: AN ANAYLSIS OF REPRESENTATION AND COST BARRIERS IN STOCK PHOTO LIBRARIES FOR HEALTH EDUCATION MATERIALS, 2021

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    Ineffective health communication can drive health disparities and limit the effectiveness of interventions to reduce health disparities. Stock photo libraries are a critical tool for developers of patient education and intervention materials. It is not clear how well stock photo libraries represent communities bearing disproportionate burdens of disease. We conducted a search using five popular stock image libraries (Adobe Stock Images, Canva, Getty Images, Microsoft Office Image Library, and Pixabay) in November 2021 to evaluate diversity and representation in health-related stock photos. We searched for five key preventive health topics: healthy eating, exercising, quitting smoking, vaccination, and pregnancy. The images (N=495) were coded for representation of perceived minoritized racial/ethnic identity, skin color using the Massey-Martin Skin Color scale, markers of high socioeconomic status (SES), and access costs. We established inter-rater coding reliability. The representation of perceived minoritized people, darker skin color, and inclusion of markers of high SES varied greatly by the search term and database. After excluding images without people or with ambiguous representation, 51.5% of images across all databases depicted a person of a perceived minoritized racial/ethnic identity. Images in databases with any paywall were significantly more likely to depict a person of perceived minoritized racial/ethnic identity, depict darker skin colors, and significantly less likely to contain markers of high SES identity than images in databases that were free to use. We found it costs more to develop quality health education materials for minoritized populations and that do not represent high SES populations. This may hinder the development of effective communication interventions

    Sequence Checking and Deduplication for Existing Fingerprint Databases

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    Biometric technology is a rapidly evolving field with applications that range from access to devices to border crossing and entry/exit processes. Large-scale applications to collect biometric data, such as border crossings result in multimodal biometric databases containing thousands of identities. However, due to human operator error, these databases often contain many instances of image labeling and classification; this is due to the lack of training and throughput pressure that comes with human error. Multiple entries from the same individual may be assigned to a different identity. Rolled fingerprints may be labeled as flat images, a face image entered into a fingerprint field or images entered in incorrect orientation (such as rotated face images, left or right iris, etc.) are common errors found large database records. Ultimately, these enrollment errors make it impossible to identify that individual upon subsequent identification encounters. Sorting through hundreds of images to check for classification errors is a tedious and time-consuming task, especially when several biometric databases are combined. Our goal is to correctly identify misclassified fingerprints using controlled embeddings and thresholds. This work provides a new perspective on image sorting as it focuses not on the traditional aspects of increasing accuracy metrics but provides a look into multiple factors through various embeddings and thresholds to provide a tool that can be used to scour large datasets with ease to provide what percentage of the images need manual correction. The proposed network provides various metric scores which allowed for analysis on the most effective embedding and thresholds to use, resulting in a proof-of-concept to be used for practical purposes in the real world
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