Abstract: Image transformation tasks such as cropping, text addition etc. are common across industries. Each industry has different business context and demands the image transformations be performed aligned to the business context. This disclosure relates to a system and method for an adaptive image transformation for a given context and maintaining aesthetic sense of the transformed image. Herein, the system is configurable and adaptive to any business context or domain. The system learns the context from available domain samples and creates an automated workflow of context-aware transformation tasks that maintains both the content and aesthetics demands of the context. Further, a saliency map is extracted for the identified RoI to append a text to the RoI based on the extracted saliency map, the calculated similarity metric for various content and aesthetic factors and various preferences of the user.

Abstract: This disclosure relates generally to system and method for hierarchical category classification of products. Generally in supervised hierarchical classification, the hierarchy structure is predefined. However, majority of the current machine learning methods either expect the model to learn the hierarchy from the data or requires separate models trained at each level taking the prediction of previous level as an additional input, thereby increasing latency in achieving training accuracy and/or requiring an explicit maintenance module to orchestrate inference and retrain multiple models (corresponding to the number of levels in the hierarchy). The disclosed method and system allows the predefined knowledge about hierarchy drive the learning process of a single model, which predicts all levels of the hierarchy. The disclosed multi-layer network model arrives at a consensus based on prediction at each level, thereby increasing the accuracy of prediction and reducing the training time.