Image Classification Using Capture-Location Sequence
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Solution Overview
Problem
Existing image classification systems fail to effectively utilize temporal and spatial correlations among images in personal collections, leading to incomplete semantic understanding and misclassification of events.
Innovation Solution
A method that combines GPS capture-location information and visual features to classify groups of temporally related images, using trace features and visual features in a multi-class boosting framework to improve event recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image clustering and classification is performed based on individual images using image-based features only, then the classification process is simple and fast, but the semantic understanding is incomplete and misclassification of events occurs
Solution Approach 1:
The patent combines multiple data sources (image features, GPS location data, timestamps) and multiple classification approaches into a unified event recognition system. This merging of heterogeneous data types and classification methods enables more accurate event recognition by leveraging complementary information from different modalities, directly addressing the limitation of incomplete semantic understanding when using image features alone.
Solution Approach 2:
The patent extends the classification approach from a single dimension (image features only) to multiple dimensions by incorporating spatial (GPS coordinates) and temporal (timestamps) dimensions. This multi-dimensional approach allows the system to capture the full context of photo collections, transforming the classification problem from analyzing isolated images to understanding sequences of images in space and time, thereby improving event recognition accuracy.
2Loss of information
If images are treated as independent without considering temporal and spatial correlation, then the processing is simpler, but the rich context information complementary to image features is lost
Solution Approach 1:
The patent performs preliminary organization of photo collections into event groups based on temporal and spatial correlations before detailed classification. By pre-grouping images that belong to the same event based on their GPS trajectories and time sequences, the system preserves context information while reducing the complexity of subsequent classification tasks, thus balancing information retention with processing efficiency.
Solution Approach 2:
The patent introduces GPS location data and timestamp information as intermediary elements that bridge individual images and their contextual relationships. These intermediaries capture the temporal and spatial correlations between images, enabling the system to retain rich context information about photo collections while maintaining manageable processing complexity through structured data representation.
3Reliability
If only image-based features are used for classification, then the feature extraction is straightforward, but the semantic complexity of events cannot be fully captured
Solution Approach 1:
The patent creates a composite feature representation by combining multiple types of data (visual features from images, spatial features from GPS coordinates, temporal features from timestamps) into a unified feature vector. This composite approach leverages the strengths of each data type while compensating for their individual limitations, thereby improving classification reliability through multi-modal feature fusion without requiring overly complex individual feature analysis pipelines.
Data Source
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AI summary
Classification of a group of temporally related images is disclosed, wherein a capture-location sequence is identified from the group of temporally related images. The capture-location-sequence information, which is associated collectively with the capture-location sequence, is compared with each of a plurality of sets of predetermined capture-location-sequence characteristics. Each set is associated with a predetermined classification. An identified classification associated with the group of temporally related images is identified based at least upon results from the comparing step; and the identified classification is stored in a processor-accessible memory system