Semantic segmentation techniques and systems are described that overcome the challenges of limited availability of training data to describe the potentially millions of tags that may be used to describe semantic classes in digital images. In one example, the techniques are configured to train neural networks to leverage different types of training datasets using sequential neural networks and use of vector representations to represent the different semantic classes.

Various embodiments of systems and methods allow a system to identify subsets of items by mixing and matching identified features in one or more other items. A system can identify features of items in an item database. The system can then calculate “fingerprints” of these features which are vectors describing the characteristics of the features. The system can present a collection of items and a user can select an item of the collection. The user can then select positive features to include in a search and/or negative features to include in the search. The system can then do a search of the database for items that contain features similar to those positive features and do not contain features similar to those negative features. The user can select features through a variety of means.

Various embodiments of systems and methods allow a system to identify subsets of items by mixing and matching identified features in one or more other items. A system can identify features of items in an item database. The system can then calculate “fingerprints” of these features which are vectors describing the characteristics of the features. The system can present a collection of items and a user can select an item of the collection. The user can then select positive features to include in a search and/or negative features to include in the search. The system can then do a search of the database for items that contain features similar to those positive features and do not contain features similar to those negative features. The user can select features through a variety of means.

Image Retrieval is an application of computer vision that deals with searching images in large databases. Conventional methods utilize the entire image to perform the image retrieval task rather than considering specific features. The embodiments herein provide a method and system for feature based image retrieval. Initially, the system receives an input image and a query label. Further, a feature specific encoder is selected from a plurality of feature specific encoders based on the query label. A first set of feature vectors are computed from the input image using the selected feature specific encoder. Further, a Locality Sensitive Hashing (LSH) value is computed from the first set of feature vectors. Finally, a plurality of matching images is obtained from a plurality database images based on a comparison between the computed feature specific LSH value and a plurality of feature specific LSH values stored in a feature specific LSH database.

Image Retrieval is an application of computer vision that deals with searching images in large databases. Conventional methods utilize the entire image to perform the image retrieval task rather than considering specific features. The embodiments herein provide a method and system for feature based image retrieval. Initially, the system receives an input image and a query label. Further, a feature specific encoder is selected from a plurality of feature specific encoders based on the query label. A first set of feature vectors are computed from the input image using the selected feature specific encoder. Further, a Locality Sensitive Hashing (LSH) value is computed from the first set of feature vectors. Finally, a plurality of matching images is obtained from a plurality database images based on a comparison between the computed feature specific LSH value and a plurality of feature specific LSH values stored in a feature specific LSH database.

Technologies generally described herein relate to a computing device for an input assistance scheme for an online marketplace posting. In one aspect, a computing device receives an input image containing an object to be posted on an online marketplace. In response to receiving the object, the computing device extracts, from the input image, feature data relating to the object. The device performs a search of an image database based on the extracted feature data to determine one or more images containing the object. The computing device obtains information data of the determined images and generates a reference dataset for the object based on the reference dataset.

Technologies generally described herein relate to a computing device for an input assistance scheme for an online marketplace posting. In one aspect, a computing device receives an input image containing an object to be posted on an online marketplace. In response to receiving the object, the computing device extracts, from the input image, feature data relating to the object. The device performs a search of an image database based on the extracted feature data to determine one or more images containing the object. The computing device obtains information data of the determined images and generates a reference dataset for the object based on the reference dataset.

A method for searching images is disclosed. The method includes obtaining a query keyword; and obtaining a target semantic embedding of the query keyword via a Semantics Aligning Network (SAN), and searching at least one target image corresponding to the query keyword according to the target semantic embedding via the SAN. The SAN is configured as a semantic embedding extractor and for providing a visual-semantics space, the visual-semantics space defines a mapping relationship between at least one image embedding and semantic embeddings, and each semantic embedding is generated based on a semantic constraint.

A method for searching images is disclosed. The method includes obtaining a query keyword; and obtaining a target semantic embedding of the query keyword via a Semantics Aligning Network (SAN), and searching at least one target image corresponding to the query keyword according to the target semantic embedding via the SAN. The SAN is configured as a semantic embedding extractor and for providing a visual-semantics space, the visual-semantics space defines a mapping relationship between at least one image embedding and semantic embeddings, and each semantic embedding is generated based on a semantic constraint.

Disclosed herein is Arrowland, a web-based software tool for inputting, managing and viewing multiomics data, such as transcriptomics, proteomics, metabolomics and fluxomics data in an interactive, intuitive and multiscale system.