يادگيري بلندمدت مبتني بر الگوهاي معنايي با بهره‌گيري از اطلاعات يادگيري کوتاه‌مدت به روش بهبود تابع شباهت در سامانه‌هاي بازيابي تصوير
محورهای موضوعی : مهندسی برق و کامپیوتر
عصمت راشدی
1
(دانشگاه تحصیلات تکمیلی صنعتی کرمان)
حسین نظام‌آبادی‌پور
2
(دانشگاه شهید باهنر کرمان)
کلید واژه: بازيابي تصوير يادگيري بلندمدت يادگيري کوتاه‌مدت الگوهاي معنايي,
چکیده مقاله :
بازيابي معنايي تصوير از مباحث مورد توجه در بازشناسي الگو است. جهت نزديک‌تر شدن سامانه بازيابي به محتواي معنايي تصاوير از روش‌هاي يادگيري کوتاه‌مدت و بلندمدت در قالب بازخورد ربط استفاده مي‌شود. در دهه اخير استفاده از يادگيري بلندمدت در سامانه‌هاي بازيابي مورد توجه زيادي قرار گرفته است و رويکردهايي در اين زمينه ارائه شده است. در اين مقاله رويکرد جديدي در يادگيري بلندمدت با ارائه روشي براي بيان الگوهاي معنايي ارائه شده است. در اين روش، الگوهاي معنايي بر پايه اطلاعات مستخرج از يادگيري کوتاه‌مدت مبتني بر بهبود تابع شباهت، تهيه و از اين اطلاعات در بهبود نتايج بازيابي در پرس و جوهاي آينده استفاده مي‌شود. علاوه بر آن، يک معيار مؤثر تعيين شباهت بين الگوهاي معنايي پيشنهادي و تصاوير براي بازيابي ارائه و روش پيشنهادي در يک پايگاه تصوير با 10000 تصوير آزموده شده است. اين روش با يک روش متداول در يادگيري بلندمدت مقايسه و نتايج ارائه شده است. نتايج آزمايش‌ها، بهبود دقت بازيابی در روش پيشنهادی نسبت به حالت بدون يادگيری بلندمدت و با يادگيری بلندمدت به روش 'iFind' را نشان مي‌دهد.
In This paper, a new scheme for long term learning in CBIR systems is proposed. In this scheme, semantic templates are extracted from information provided through relevance feedback process for short-term learning which use similarity refinement techniques. This information will be used as semantic templates in future retrieval sessions to improve the precision of the CBIR system. Also, a similarity function is introduced to calculate the similarity between queries and semantic templates. The proposed method is examined on a database with 10000 color images. The experimental results and comparison with ‘iFind’ method, confirm the effectiveness of the proposed method.
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