Abstract:-

In recent years, online shopping has grown exponentially and huge number of images are available online. Hence, it is necessary to recommend various product images to aid the user in effortless and efficient access to the desired products. In this paper, we present image recommendation framework with user relevance feedback session and visual features (IR_URFS_VF) to extract relevant images based on user inputs. User feedback is retrieved from image search history with clicked and un-clicked images. Image features are computed off-line and later used to find relevance between images. The relevance between images is determined by cosine similarity and are ranked based on clicked frequency and similarity score between images. Experiments results show that IR_URFS_VF outperforms CBIR method by providing more relevant ranked images to the user input query.

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Conclusion:-

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• We engineered an Image Recommendation model with category clustering, ranking based on user relevance (MATLAB, Java, Python).

 

• We achieved a 22.8% improvement using our model of Image Relevance Feedback Sessions with Image Search history along with visual features.

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Publication Link:-​

https://link.springer.com/article/10.1007/s13735-016-0111-x