Sketch Based Image Retrieval using Deep Learning Based Machine Learning
Deepika Sivasankaran1, Sai Seena P2, Rajesh R3, Madheswari Kanmani4
1Deepika Sivasankaran*, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
2Sai Seena P, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
3Rajesh R, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
4Madheswari Kanmani, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
Manuscript received on May 15, 2021. | Revised Manuscript received on May 22, 2021. | Manuscript published on June 30, 2021. | PP: 79-86 | Volume-10 Issue-5, June 2021. | Retrieval Number: 100.1/ijeat.E26220610521 | DOI: 10.35940/ijeat.E2622.0610521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sketch based image retrieval (SBIR) is a sub-domain of Content Based Image Retrieval(CBIR) where the user provides a drawing as an input to obtain i.e retrieve images relevant to the drawing given. The main challenge in SBIR is the subjectivity of the drawings drawn by the user as it entirely relies on the user’s ability to express information in hand-drawn form. Since many of the SBIR models created aim at using singular input sketch and retrieving photos based on the given single sketch input, our project aims to enable detection and extraction of multiple sketches given together as a single input sketch image. The features are extracted from individual sketches obtained using deep learning architectures such as VGG16 , and classified to its type based on supervised machine learning using Support Vector Machines. Based on the class obtained, photos are retrieved from the database using an opencv library, CVLib , which finds the objects present in a photo image. From the number of components obtained in each photo, a ranking function is performed to rank the retrieved photos, which are then displayed to the user starting from the highest order of ranking up to the least. The system consisting of VGG16 and SVM provides 89% accuracy.
Keywords: Sketch Based Image Retrieval, Machine Learning, Deep Learning, Content Based Image Retrieval
Scope of the Article: Deep Learning