Search Results for "content based image retrieval using matlab code"
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Showing 6 open source projects for "content based image retrieval using matlab code"
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AnaSP
AnaSP, a MATLAB software suite to ANAlyse SPheroids' features
... modular architecture and graphical user interface make it attractive also for non-Computer Scientist people, and suitable for both high content screenings and occasional spheroid-based experiments. Please, when using AnaSP, cite: "F. Piccinini, AnaSP: a software suite for automatic image analysis of multicellular spheroids. Computer Methods and Programs in Biomedicine, 119(1):43-52, 2015." Requirements: MATLAB R2022a or later versions. -
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ReViSP
ReViSP, a 3D volume rendering MATLAB tool for multicellular spheroids
Reconstruction and Visualization from a Single Projection (ReViSP) tool: a 3D volume rendering method we developed to reconstruct the 3D shape of multicellular spheroids, besides estimating the volume by counting the voxels (3D pixels) fully included in the 3D surface. ReViSP is written in MATLAB (The MathWorks, Inc., Massachusetts, USA) and the source code is freely provided. Requirements for running ReViSP from the source code: MATLAB 2020a and Image Processing Toolbox 11.1 or later... -
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HashingBaselineForImageRetrieval
Various hashing methods for image retrieval and serves as the baseline
This repository provides baseline implementations of deep supervised hashing methods for image retrieval tasks using PyTorch. It includes clean, minimal code for several hashing algorithms designed to map images into compact binary codes while preserving similarity in feature space, enabling fast and scalable retrieval from large image datasets. -
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Netvlad
NetVLAD: CNN architecture for weakly supervised place recognition
NetVLAD is a deep learning-based image descriptor framework developed by Relja Arandjelović for place recognition and image retrieval. It extends standard CNNs with a trainable VLAD (Vector of Locally Aggregated Descriptors) layer to create compact, robust global descriptors from image features. This implementation includes training code and pretrained models using the Pittsburgh and Tokyo datasets. -
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Content-based Image Retrieval (CBIR) consists of retrieving visually similar images to a given query image from a database of images. It is done by comparing selected visual features such as color, texture and shape from the image database.
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This is a Content Based Image Retrieval Interface with only color features implemented. This is part of a thesis work to analyze the different color features and observe the performance on mainly corel5k images.
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