BACKGROUND

Advancements in computing devices and image analysis techniques have led to a variety of innovations in identifying digital images that are visually similar. For example, image analysis systems are now able to analyze high-resolution digital images to identify objects within the images and search through terabytes of information stored in digital image databases to identify other digital images that depict the same or similar objects.

Despite these advances however, conventional image analysis systems continue to suffer from a number of disadvantages, particularly in the accuracy and flexibility of identifying similar digital images. For instance, while conventional image analysis systems can identify the same objects in two different digital images, these systems often disregard other aspects of the images (e.g., backgrounds, spatial arrangement of objects, and other visual attributes of the images). Indeed, because conventional image analysis systems often rely solely on semantic content to classify images based on various image tags, these systems are too object-focused in their analysis. As a result, conventional image analysis systems often produce inaccurate results when determining the visual similarity of two images. This is a particularly significant problem because, due to this inaccuracy, users of conventional image analysis systems are often required to spend an inordinate amount of time performing excessive user actions searching through match results before locating desirable image matches.

In addition, conventional image analysis systems are often inflexible. Indeed, as mentioned, conventional image analysis systems are often one-dimensional in that they only match digital images based on identifying particular objects within the images. Many of these systems also require very specific input (e.g., a single digital image) to use as basis for finding matching images. In cases where a user wants to find similar images for more than one input image, many conventional image analysis systems require performing multiple single-image searches and/or retraining an analysis model to accommodate multiple input images. As a result of their inflexible nature, these conventional systems are often incapable of tailoring image matching to the needs of a user beyond searching individual images for particular objects.

Thus, there are several disadvantages with regard to conventional image analysis systems.

SUMMARY

Embodiments described herein provide benefits and solve one or more of the foregoing or other problems in the art by providing systems, methods, and non-transitory computer readable media that utilize a deep neural network-based model to accurately and flexibly identify digital images that share visual attributes. For instance, the disclosed systems can match digital images based at least on visual attributes such as spatial selectivity, image composition, and/or object count. In addition, the disclosed systems can identify digital image matches based on a composite analysis of visual attributes from multiple digital images. While this summary refers to systems for simplicity, the features summarized herein are also attributable to the various methods and non-transitory computer readable media of this disclosure.

To illustrate, in some embodiments, the disclosed systems receive a user selection of a query digital image and at least one of spatial selectivity, image composition, and/or object count to use to identify visually similar digital images. The systems further utilize a trained deep neural network-based model to generate a deep neural network-based representation of the query digital image. Based on the deep neural network-based representation of the query digital image, the disclosed systems identify similar digital images for the query digital image from a digital image database.

In some embodiments, the disclosed systems receive a multi-image query identifying multiple query images and then identifies digital images that are similar to the combined visual attributes of the multiple query images. In some embodiments, the disclosed systems facilitate multi-query similarity by generating a compound feature vector that represents visual attributes of the multiple query images. The disclosed systems utilize the trained deep neural network-based model to identify similar digital images based on the compound feature vector.

The disclosed systems, methods, and non-transitory computer-readable media provide several advantages over conventional image analysis systems. For example, the disclosed systems are able to determine image similarity based on multiple visual attributes such as spatial selectivity, image composition/layout, and object count. As a result, the disclosed systems identify images that are more visually similar to a query digital image. Furthermore, because the disclosed systems are capable of receiving indications of visual attributes for one or more query digital images, the disclosed systems can flexibly identify matching digital images based on a variety of user-selected factors (e.g., multiple query digital images and various visual attributes).

Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of such example embodiments.

