Event-Based Semantic Image Classification Using Time and Content Features

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Solution Overview

Problem

Consumers face challenges in organizing and retrieving large collections of digital images and videos due to the lack of efficient automated tools that can understand image content and enable searching by semantic concepts such as events, people, and places, requiring manual tagging and labor-intensive processes.

Innovation Solution

A method for automatically classifying digital images into semantic event categories by generating an event representation, computing global time-based and content-based features, and using these features to classify images into pre-determined categories, enabling automated labeling and search functionality.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual tagging is used to label images with event categories, then image organization and retrieval accuracy is improved, but labor intensity and time consumption increase

Engineering Contradiction:

Improveimage classification accuracyVSAvoidmanual tagging time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system automatically classifies images into event categories using content-based features and machine learning models, enabling the image database to self-organize without manual intervention. The classifier processes images autonomously by extracting features such as color histograms, texture patterns, and object recognition results to assign event labels.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of tagging images is replaced by an automated computational system that uses content-based image retrieval techniques, feature extraction algorithms, and classification models to perform event categorization automatically.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If low-level features such as color and texture are used for image classification, then computational efficiency is improved, but semantic correlation with image content deteriorates

Engineering Contradiction:

Improveclassification speedVSAvoidsemantic content correlation

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The system merges multiple feature types including color histograms, texture descriptors, shape features, and object recognition results into a comprehensive feature vector. This combination allows the system to maintain computational efficiency while capturing both low-level visual properties and high-level semantic content for improved event classification.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The classification system uses composite feature representations that integrate multiple types of image attributes (color, texture, shape, objects) similar to how composite materials combine different properties. This multi-faceted feature composition enables the system to achieve both speed and semantic accuracy.

Inventive Principle:

Principle #40Composite materials

3Loss of information

If semantic-level features are computed from images, then correlation with image meaning is improved, but computational complexity increases

Engineering Contradiction:

Improvesemantic content correlationVSAvoidfeature computation complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system segments the image analysis process into distinct stages: low-level feature extraction (color, texture), mid-level object recognition, and high-level event classification. Each stage processes specific features independently, reducing overall computational complexity while maintaining semantic correlation through the hierarchical progression.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary feature extraction and preprocessing steps before main classification, computing basic image properties such as color histograms and texture descriptors in advance. These pre-computed features serve as foundations for more complex semantic analysis, reducing the computational burden during the main classification phase.

Inventive Principle:

Principle #10Preliminary action

4Productivity

If automated classification tools are implemented, then image organization efficiency is improved, but system complexity increases

Engineering Contradiction:

Improveimage organization efficiencyVSAvoidautomated system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The classification system is designed to handle multiple image types and event categories using a unified framework. The same feature extraction and classification pipeline processes diverse images (photographs, scans, videos) and categorizes them into various event types (birthdays, vacations, sports), reducing the need for separate specialized systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS8611677B2Method for event-based semantic classification

Publication Date: 2013.12.17 MONUMENT PEAK VENTURES LLC

AI summary

A method of automatically classifying images in a consumer digital image collection, includes generating an event representation of the image collection; computing global time-based features for each event within the hierarchical event representation; computing content-based features for each image in an event within the hierarchical event representation; combining content-based features for each image in an event to generate event-level content-based features; and using time-based features and content-based features for each event to classify an event into one of a pre-determined set of semantic categories.