Object Recognition Using Multi-Modal Feature Fusion
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Solution Overview
Problem
Conventional object recognition systems struggle with identifying feature-sparse or texture-limited objects, such as electronics and kitchen appliances, as they lack distinctive local features, leading to unsuccessful or inaccurate recognition.
Innovation Solution
The system employs a multi-modal approach by complementing local features with non-local features like color, region-based, shape-based, and global features, and uses machine learning techniques at various stages of the object recognition pipeline, including image segmentation, matching, and vocabulary building, to enhance recognition capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object recognition approaches using local features are applied, then recognition speed and simplicity are maintained, but recognition accuracy deteriorates for feature-sparse or texture-limited objects
Solution Approach 1:
The patent segments the feature extraction process into multiple independent feature types (local features, global features, color features, shape features, texture features). Each feature type is extracted and processed separately, then combined to form a comprehensive object representation. This segmentation allows the system to handle feature-sparse objects by utilizing multiple feature dimensions rather than relying solely on local features.
Solution Approach 2:
The patent merges multiple feature types (local, global, color, shape, texture) into a unified feature representation for object recognition. By combining these diverse feature sources, the system creates a more robust and comprehensive description of objects, particularly improving recognition accuracy for feature-sparse objects that lack sufficient local features alone.
2Reliability
If multiple feature types and machine learning techniques are incorporated, then object recognition accuracy for feature-sparse objects is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies machine learning techniques during the offline training phase to pre-compute and store feature descriptors and build vocabulary trees. This preliminary action prepares the system in advance, so that during online recognition, the complex machine learning models can be efficiently applied without real-time computational burden, thus improving reliability while managing system complexity.
Solution Approach 2:
The patent replaces traditional mechanical feature matching approaches with machine learning-based models. Instead of simple template matching or hand-crafted feature comparison, the system uses trained machine learning models to automatically learn and recognize object features, improving reliability for feature-sparse objects while the models handle the complexity internally.
3Productivity
If local features alone are used for object recognition, then processing speed is maintained, but recognition success rate decreases for objects with uniform surfaces
Solution Approach 1:
The patent extends the feature extraction from a single dimension (local features only) to multiple dimensions by incorporating global features, color features, shape features, and texture features. This multi-dimensional approach allows the system to detect and recognize objects with uniform surfaces by utilizing alternative feature dimensions that capture different aspects of object appearance, thereby improving feature detection precision without significantly compromising processing speed.
Data Source
AI summary
An object recognition system can be adapted to recognize subject matter having very few features or limited or no texture. A feature-sparse or texture-limited object can be recognized by complementing local features and/or texture features with color, region-based, shape-based, three-dimensional (3D), global, and/or composite features. Machine learning algorithms can be used to classify such objects, and image matching and verification can be adapted to the classification. Further, multiple modes of input can be integrated at various stages of the object recognition processing pipeline. These multi-modal inputs can include user feedback, additional images representing different perspectives of the object or specific regions of the object including a logo or text corresponding to the object, user behavior data, location, among others.