ObjectSense: a scalable multi-objects recognition system based on partial-duplicate image retrieval

Shuang Wang, Yunfeng Xue, Lingyang Chu, Yuhao Jiang, Shuqiang Jiang

Abstract: In this demo, we present ObjectSense, a scalable object recognition system that recognizes multiple objects present in a static image or in the camera frames. Instead of applying learning based recognition framework, this system identifies objects through Partial-Duplicate Image Retrieval (PDIR) based method. First, objects are identified by measuring the similarity between an incoming image and reference image corpus that are labeled with the objects. To compute image similarities, we explore the Consistency Graph Model (CGM), which robustly rejects spatially inconsistent feature matches with the advantage of orientations and positions of local features. Then a kNN voting method is used to decide the object category based on the quantized image similarities. ObjectSense is scalable with promisingly high recall and accuracy, which fits well into recognition-guided shopping and human computer interaction. We built ObjectSense on two platforms, PC and Android.