Multiple-Cue Object Recognition on outside datasets

This work builds upon the fact that robots can observe humans interacting with the objects in their environment, and that humans provide numerous non-visual cues to the identity of objects. In previous work, we outlined a Multiple-Cue Object Recognition (MCOR) algorithm which attempted to use multiple features of any type to produce more robust object recognition. All results so far reported with MCOR have been on data collected by ourselves. In this work, we introduce new advancements in the MCOR algorithm to increase its effectiveness and ability to deal with complex real data from outside datasets. These advancements include the integration of Scale-Invariant Feature Transform (SIFT) features and an improvement in training. To demonstrate the effectiveness of the MCOR framework, we first show a comparison of the MCOR algorithm to an outside dataset to show its basic advantages. We then demonstrate the advanced MCOR features on real television video datasets in particular cooking.

Authors

• Sarah S. Aboutalib
• Manuela Veloso

Keywords

• Visualization
• Object recognition
• Training
• Feature extraction
• Dictionaries
• Histograms
• Image color analysis
• Human Interaction
• Object Identification
• Recognition Algorithm
• Video Dataset
• Scale-invariant Feature Transform
• Training Improvement
• Types Of Information
• Visual Cues
• Bounding Box
• Video Clips
• Object Classification
• Scale Changes
• Real-world Datasets
• Viewing Angle
• Action Recognition
• Object Segmentation
• Color Information
• Image Descriptors
• Previous Algorithms
• Camera Angle
• Objects In Order
• Scene Classification
• University Of Maryland
• Cue Values
• Objective Definition
• Visual Features