Camera Metadata and Histogram Scene Classification

Solution Overview

Problem

Existing automatic scene classification systems are computationally intensive and require significant memory resources, making them unsuitable for embedded environments, and struggle to classify images into scene types like night scenes and snow scenes where low-level feature descriptors cannot be determined.

Innovation Solution

The method employs metadata-based classification using light and brightness metadata, combined with histogram-based classification to efficiently classify images into scene types, reducing computational and memory requirements and utilizing metadata predicates and histogram predicates to verify classifications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If content-based scene classification methods are used, then classification accuracy is improved, but computational complexity and memory requirements increase significantly

Engineering Contradiction:

Improveclassification accuracyVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the classification process into two independent components: metadata-based classification and content-based classification. The metadata-based classifier first determines scene type using camera metadata, and only if needed, the content-based classifier processes image features. This segmentation allows the system to achieve high accuracy while reducing computational complexity by avoiding full content-based analysis for all images.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies preliminary action by using metadata-based classification as a first step before content-based classification. The metadata classifier quickly filters and pre-classifies images, and only ambiguous or uncertain cases proceed to the more computationally intensive content-based analysis. This preliminary filtering reduces the overall computational burden while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If content-based scene classification methods are used, then classification accuracy is improved, but memory resources are significantly consumed

Engineering Contradiction:

Improveclassification accuracyVSAvoidmemory resources

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent segments the classification process into two independent components: metadata-based classification and content-based classification. The metadata-based classifier first determines scene type using camera metadata, and only if needed, the content-based classifier processes image features. This segmentation allows the system to achieve high accuracy while reducing computational complexity by avoiding full content-based analysis for all images.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies preliminary action by using metadata-based classification as a first step before content-based classification. The metadata classifier quickly filters and pre-classifies images, and only ambiguous or uncertain cases proceed to the more computationally intensive content-based analysis. This preliminary filtering reduces the overall computational burden while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If low-level feature descriptors are used for scene classification, then classification capability is improved, but inability to determine features for certain scenes (night scenes, snow scenes) occurs

Engineering Contradiction:

Improveclassification capabilityVSAvoidfeature determination reliability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent introduces metadata as an intermediary that bridges the gap when content-based features fail. For scenes like night scenes and snow scenes where low-level features cannot be reliably determined, the system uses camera metadata (exposure time, aperture, ISO, brightness values) as an alternative basis for classification. This intermediary approach ensures reliable classification across all scene types.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent changes the classification parameters from relying solely on image content features to incorporating camera metadata parameters. By using metadata parameters such as exposure time, aperture value, ISO sensitivity, and brightness values, the system can reliably classify difficult scenes where traditional feature extraction fails, thus improving overall classification reliability.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS7668369B2Using camera metadata to classify images into scene type classes

Publication Date: 2010.02.23 HEWLETT PACKARD DEVELOPMENT COMPANY LP

AI summary

In one aspect, a metadata-based classification result is obtained based on metadata associated with an image. A histogram-based classification result is determined based on a histogram of intensity values derived from the image. The image is classified into a scene type class based on the metadata-based classification result and the histogram-based classification result. In another aspect, a first condition is applied on a first one of metadata associated with an image corresponding to a measure of amount of light received in capturing the image to obtain a first metadata-based classification result. A second condition is applied on a second one of the metadata corresponding to a measure of brightness of the image to obtain a second metadata-based classification result. The image is classified into a scene type class based on the first and second metadata-based classification results.