{"id":25007852,"url":"https://github.com/samashi47/content-based-image-retrieval","last_synced_at":"2026-05-20T07:40:07.284Z","repository":{"id":268382798,"uuid":"898438124","full_name":"Samashi47/content-based-image-retrieval","owner":"Samashi47","description":"A content-based image retrieval (CBIR) system with Bayesian relevance feedback, utilizing color, shape, and texture descriptors for efficient image search.","archived":false,"fork":false,"pushed_at":"2025-01-25T19:55:40.000Z","size":56646,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-05T09:55:06.127Z","etag":null,"topics":["bayesian-inference","cbir","image-indexing","opencv","python3","relevance-feedback"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Samashi47.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.bib","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-12-04T11:51:57.000Z","updated_at":"2025-01-25T19:55:43.000Z","dependencies_parsed_at":"2024-12-16T12:38:47.722Z","dependency_job_id":"62468fea-0d0d-41d8-b285-cb75ef1a0b44","html_url":"https://github.com/Samashi47/content-based-image-retrieval","commit_stats":null,"previous_names":["samashi47/content-based-image-retrieval"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Samashi47/content-based-image-retrieval","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Samashi47%2Fcontent-based-image-retrieval","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Samashi47%2Fcontent-based-image-retrieval/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Samashi47%2Fcontent-based-image-retrieval/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Samashi47%2Fcontent-based-image-retrieval/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Samashi47","download_url":"https://codeload.github.com/Samashi47/content-based-image-retrieval/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Samashi47%2Fcontent-based-image-retrieval/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33250375,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-20T04:48:54.280Z","status":"ssl_error","status_checked_at":"2026-05-20T04:48:10.851Z","response_time":356,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-inference","cbir","image-indexing","opencv","python3","relevance-feedback"],"created_at":"2025-02-05T02:56:00.749Z","updated_at":"2026-05-20T07:40:07.226Z","avatar_url":"https://github.com/Samashi47.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# content-based-image-retrieval\n\nWelcome to our web application designed to implement a robust Content-Based Image Retrieval (CBIR) system that enables efficient image search and management through visual features and relevance feedback mechanisms. Users can upload, download, delete, and categorize images into predefined classes, as well as generate new images by applying transformations like cropping and scaling. The system computes and displays visual descriptors for images, including color histograms, dominant colors, Gabor texture filters, Hu moments, and additional custom descriptors. It supports both basic search to retrieve visually similar images and an advanced Bayesian relevance feedback mechanism to iteratively refine results, providing an intuitive and dynamic way to explore the RSSCN7 dataset, which consists of 2,800 images categorized into seven scene types such as Residential, Forest, and Industry.\n\n## Installation\n\nTo start off, clone this branch of the repo into your local:\n\n```shell\ngit clone https://github.com/Samashi47/content-based-image-retrieval.git\n```\n\n```shell\ncd content-based-image-retrieval\n```\n\n### Backend\n\nAfter cloning the project, if you are using Python 3.12.z with shared libraries enabled, you can checkout to the `edge` branch, using the latest pymc version:\n\n```shell\ngit checkout edge\n```\n\nIf not, you can stay on the `main` branch.\n\nThen, create a virtual environment:\n\n```shell\ncd apps/api\n```\n\n**Windows**\n\n```shell\npy -3 -m venv .venv\n```\n\n**MacOS/Linus**\n\n```shell\npython3 -m venv .venv\n```\n\nThen, activate the env:\n\n**Windows**\n\n```shell\n.venv\\Scripts\\activate\n```\n\n**MacOS/Linus**\n\n```shell\n. .venv/bin/activate\n```\n\nYou can run the following command to install the dependencies:\n\n```shell\npip3 install -r requirements.txt\n```\n\nAfter installing the dependencies, you should specify the mongodb connection string in the `.env` file:\n\n```shell\ntouch .env\n```\n\n```env\nMONGO_URL=\u003curl\u003e\n```\n\nThen, you can run the following command to start the backend:\n\n```shell\npython server.py\n```\n\n### Frontend\n\nOpen another terminal:\n\n```shell\ncd apps/app\n```\n\nThen, run the following command to install the dependencies:\n\n```shell\npnpm install\n```\n\nthen, run the following command to start the frontend, if you have angular cli installed globally:\n\n```shell\nng serve\n```\n\nif not, you can run the following command:\n\n```shell\npnpm run ng serve\n```\n\nThen, open your browser and navigate to `http://localhost:4200/` to see the app running.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamashi47%2Fcontent-based-image-retrieval","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsamashi47%2Fcontent-based-image-retrieval","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamashi47%2Fcontent-based-image-retrieval/lists"}