SDLNet: Statistical Deep Learning Network for Co-Occurring Object Detection and Identification

TL;DR

SDLNet is a novel framework that combines deep learning and statistical analysis to detect and identify co-occurring objects in images, improving multi-label object recognition.

Contribution

It introduces a two-stage approach integrating multilabel detection with co-occurrence matrix analysis for better object co-occurrence understanding.

Findings

Effective co-occurrence detection on Pascal VOC and MS-COCO datasets.

Improved multi-label object recognition accuracy.

Demonstrated the utility of combining deep learning with statistical co-occurrence analysis.

Abstract

With the growing advances in deep learning based technologies the detection and identification of co-occurring objects is a challenging task which has many applications in areas such as, security and surveillance. In this paper, we propose a novel framework called SDLNet- Statistical analysis with Deep Learning Network that identifies co-occurring objects in conjunction with base objects in multilabel object categories. The pipeline of proposed work is implemented in two stages: in the first stage of SDLNet we deal with multilabel detectors for discovering labels, and in the second stage we perform co-occurrence matrix analysis. In co-occurrence matrix analysis, we learn co-occurrence statistics by setting base classes and frequently occurring classes, following this we build association rules and generate frequent patterns. The crucial part of SDLNet is recognizing base classes and making consideration for co-occurring classes. Finally, the generated co-occurrence matrix based on frequent patterns will show base classes and their corresponding co-occurring classes. SDLNet is evaluated on two publicly available datasets: Pascal VOC and MS-COCO. The experimental results on these benchmark datasets are reported in Sec 4.