Text and Image Query System for Image Datasets

Author(s): Kejitan Dontas, Krishna Kumar Tiwari

Abstract

In this paper, we have described the approach we used to build an end to end system for text and image query on image data sets, we also call it TIQS (Text & Image Query System). The system retrieves relevant images from a (given annotated) data set based on a sample image or a text query. We have used images from two data sets. 1. Visual Genome dataset from Stanford [1][3] and 2. ADE20K data set from CSAIL MIT [4][5][6]. The Visual Genome data set is fully annotated and can be directly used for text queries. To respond to image-based queries, the images are segmented and annotated (into 150 categories) using PSPNET[9]. When a sample image is presented to retrieve similar images, the sample image too is segmented and annotated using PSPNET. A novel similarity score is defined between the sample image and images in the database, and the images with high similarity scores are presented to the user.

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