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Enhancing Content-Based Image Retrieval: A Comprehensive Project Proposal

This proposal outlines strategies to improve content-based image retrieval methods in the wake of increasing digital imaging and challenges in locating images online. We explore advanced feature extraction techniques, including color histograms, texture analysis, and semantic feedback. An evaluation of existing systems like MIT Photobook and IBM QBIC highlights gaps in user interaction and search quality. The proposed solution aims to combine text and content search more effectively, allowing users to define areas of interest and enabling the creation of an enriched image database for better retrieval results.

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Enhancing Content-Based Image Retrieval: A Comprehensive Project Proposal

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Presentation Transcript

  1. Content-Based Image Retrieval Project Proposal by Charlie Neo

  2. Problem • The Growth of Digital Imaging • Difficulty of Locating Images on the Web • Feature Extraction • Automated Object Recognition

  3. Work Done • MIT Photobook • IBM QBIC • Virage (Altavista) • Excalibur (Yahoo) • VisualSEEk • Etc.

  4. How is it done • Color (Histogram) • Texture • Shape/Structure • Spatial Location • Statistical • Wavelet • Semantic • Feedback

  5. My Proposal • Better Combination of Text and Content Search • Better User Interaction • User Define Area of Interest

  6. Evaluation • Create Image Database • Satisfactory Result • Survey • Manually Assign Similar Images

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