Abstract: In apparel search context, process of finding a similar item out of thousands of other items is a cumbersome and computationally heavy process. In order to build a deep learning model that can perform the similarity search, hundreds of training images per Stock Keeping Unit (SKU) are required. Due to shortage of training data, this approach fails to generate a deep learning model that can perform the similarity search in intended manner. The existing approaches may also require domain experts to perform classification of apparels, so as to generate the training data. The method and system disclosed herein provide an approach in which positive images and negative images are generated from each query image, which in turn are used for generating a training data. The training data is then used to generate a deep learning model, which is used to perform the similarity search.

Abstract: Planograms are used to create consistency between store locations, to provide proper shelf space allocation, to improve visual merchandising appeal, and to create product-pairing suggestions. Existing solutions do not have a way to accurately estimate the scale of magnification of the object in the shelf image, so unable to distinguish between size variants of the same product. A system and method for facilitating planogram compliance for inventory management in a retail store have been provided. The scales are calculated with use of a vector convergence technique followed by a center clustering which automatically removes outliers. Initially disclosure comprises calculation of scales and centers, then generation of region proposals using those scales and centers, Next, classification of the regions proposed and generation of similarity scores, and on the basis of similarity scores conflict resolution is performed among overlapped region proposals using non-maximal suppression.

Abstract: Examples of a data migration system are provided. The system may receive a data migration requirement. The system may sort the data stored in the on-premise data warehouse into a plurality of data domains. The system may map the plurality of data domains to a usage parameter index and a storage parameter index to determine a data usage pattern and a data storage pattern. The system may evaluate the data storage pattern and the data usage pattern to determine a data assessment index. The system may determine a data migration index from mapping a plurality of cloud platforms to the data assessment index. The system may determine a data migration model compatible with the data assessment index. The system may generate a data migration output comprising the layout for transferring data stored in the on-premise data warehouse to a compatible cloud platform to resolve the data migration requirement.