APPARATUS AND METHOD FOR BLOCKIING OBJECTIONABLE IMAGE ON BASIS OF MULTIMODAL AND MULTISCALE FEATURES

• US 20110150328A1
• Filed: 12/13/2010
• Published: 06/23/2011
• Est. Priority Date: 12/21/2009
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: Lim, Jae Deok, Choi, Byeong Cheol, Chung, Byung Ho, HAN, Seung Wan
• Application Number:
  US12/966,230
• Publication Number:
  US 20110150328A1
• Time in Patent Office: Days
• US Class Current: 382/165
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59

Abstract

Provided are an apparatus and method for blocking an objectionable image on the basis of multimodal and multiscale features. The apparatus includes a multiscale feature analyzer for analyzing multimodal information extracted from image training data to generate multiscale objectionable and non-objectionable features, an objectionability classification model generator for compiling statistics on the generated objectionable and non-objectionable features and performing machine learning to generate multi-level objectionability classification models, an objectionability determiner for analyzing multimodal information extracted from image data input for objectionability determination to extract at least one of multiscale features of the input image, and comparing the extracted feature with at least one of the multi-level objectionability classification models to determine objectionability of the image, and an objectionable image blocker for blocking the input image when it is determined that the image is objectionable.

Claims

1. An apparatus for blocking an objectionable image on the basis of multimodal and multiscale features, comprising:

a multiscale feature analyzer for analyzing multimodal information extracted from image training data to generate multiscale objectionable and non-objectionable features;

an objectionability classification model generator for compiling statistics on the generated objectionable and non-objectionable features and performing machine learning to generate multi-level objectionability classification models;

an objectionability determiner for analyzing multimodal information extracted from image data input for objectionability determination to extract at least one of multiscale features of the input image, and comparing the extracted feature with at least one of the multi-level objectionability classification models to determine objectionability of the image; and

an objectionable image blocker for blocking the input image when it is determined that the image is objectionable.

2. The apparatus of claim 1, wherein the multiscale feature analyzer includes:

a coarse-grained granularity feature analyzer for analyzing degrees of color complexity, texture complexity, and shape complexity of the image training data to generate a complexity-based feature;

a middle-grained granularity feature analyzer for analyzing skin color, face, and edge information, and a Motion Picture Experts Group (MPEG)-7 descriptor included in the image training data to generate a single-modal-based low-level feature; and

a fine-grained granularity feature analyzer for detecting objects from the image training data and analyzing an objectionable meaning of the objects and a relationship between the objects to generate a multimodal-based high-level feature.

3. The apparatus of claim 2, wherein the coarse-grained granularity feature analyzer includes:

a color complexity analyzer for analyzing the degree of color complexity of the image training data;

a texture complexity analyzer for analyzing the degree of texture complexity of the image training data;

a shape complexity analyzer for analyzing the degree of shape complexity of the image training data; and

a complexity-based feature extractor for extracting the complexity-based feature according to a type and category of the image training data on the basis of the analyzed degrees of color, texture, and shape complexities.

4. The apparatus of claim 2, wherein the middle-grained granularity feature analyzer includes:

a skin color detector for detecting the skin color information from the image training data;

a face detector for detecting the face information from the image training data;

an edge detector for detecting the edge information from the image training data;

an MPEG-7 descriptor extractor for extracting the MPEG-7 descriptor from the image training data; and

a single-modal-based low-level feature generator for analyzing the skin color, face, and edge information and the MPEG-7 descriptor to generate the single-modal-based low-level feature according to a type and category of image training data.

5. The apparatus of claim 2, wherein the fine-grained granularity analyzer includes:

an object detector for detecting object information from the image training data;

an object meaning analyzer for analyzing the objectionable meaning of the detected objects;

an object relationship analyzer for analyzing the relationship between the detected objects; and

a multimodal-based high-level feature generator for generating the multimodal-based high-level feature according to a type and category of the image training data on the basis of the analyzed objectionable meaning and the analyzed relationship between the objects.

6. The apparatus of claim 2, wherein the objectionability classification model generator includes:

a low-level objectionability classification model generator for generating a low-level objectionability classification model using the complexity-based feature generated by the coarse-grained granularity feature analyzer;

a mid-level objectionability classification model generator for generating a mid-level objectionability classification model using the single-modal-based low-level feature generated by the middle-grained granularity feature analyzer; and

a high-level objectionability classification model generator for generating a high-level objectionability classification model using the multimodal-based high-level feature generated by the fine-grained granularity feature analyzer.

7. The apparatus of claim 1, wherein the objectionability determiner includes:

a coarse-grained granularity feature extractor for analyzing degrees of color complexity, texture complexity, and shape complexity of the input image data to extract a complexity-based feature;

a middle-grained granularity feature extractor for analyzing skin color, face, and edge information and a Motion Picture Experts Group (MPEG)-7 descriptor included in the input image data to extract a single-modal-based low-level feature;

a fine-grained granularity feature extractor for detecting objects from the input image data and analyzing an objectionable meaning of the detected objects and a relationship between the detected objects to extract a multimodal-based high-level feature; and

an image objectionability determiner for comparing at least one multiscale feature extracted by at least one of the coarse-grained granularity feature extractor, the middle-grained granularity feature extractor, and the fine-grained granularity feature extractor with at least one of the multi-level objectionability classification models to determine objectionability of the image.

8. The apparatus of claim 7, wherein a part or all of the coarse-grained granularity feature extractor, the middle-grained granularity feature extractor, and the fine-grained granularity feature extractor are selected according to a type and category of the input image data to selectively extract at least one of the multiscale features of the input image data.

9. The apparatus of claim 7, wherein the objectionability determiner selects at least one of a low-level objectionability classification model, a mid-level objectionability classification model, and a high-level objectionability classification model according to a type and category of the input image data, and compares the selected objectionability classification model with the feature of the input image data.

10. A method of blocking an objectionable image on the basis of multimodal and multiscale features, comprising:

analyzing multimodal information extracted from image training data to generate multiscale objectionable and non-objectionable features;

compiling statistics on the generated objectionable and non-objectionable features and performing machine learning on the generated objectionable and non-objectionable features to generate multi-level objectionability classification models;

analyzing multimodal information about image data input for objectionability determination to extract at least one of multiscale features of the input image;

comparing the at least one multiscale feature extracted from the input image data with at least one of the multi-level objectionability classification models to determine objectionability of the input image; and

blocking the input image when it is determined that the image is objectionable.

11. The method of claim 10, wherein generating the multiscale objectionable and non-objectionable features includes:

analyzing degrees of color complexity, texture complexity, and shape complexity of the image training data to generate a complexity-based feature;

analyzing skin color, face, and edge information, and a Motion Picture Experts Group (MPEG)-7 descriptor included in the image training data to generate a single-modal-based low-level feature; and

detecting objects from the image training data and analyzing an objectionable meaning of the objects and a relationship between the objects to generate a multimodal-based high-level feature.

12. The method of claim 11, wherein compiling the statistics on the generated objectionable and non-objectionable features and performing the machine learning on the generated objectionable and non-objectionable features to generate the multi-level objectionability classification models includes:

generating a low-level objectionability classification model using the complexity-based feature;

generating a mid-level objectionability classification model using the single-modal-based low-level feature; and

generating a high-level objectionability classification model using the multimodal-based high-level feature.

13. The method of claim 10, wherein extracting the at least one of multiscale features of the input image includes performing at least one of a step of analyzing degrees of color complexity, texture complexity, and shape complexity of the input image data and extracting a complexity-based feature on the basis of the analyzed degrees of the complexities, a step of extracting skin color, face, edge, and Motion Picture Experts Group (MPEG)-7 descriptor information from the input image data and extracting a single-modal-based low-level feature on the basis of the extracted information, and a step of analyzing object information, meaning information, and inter-object relationship information and extracting a multimodal-based high-level feature on the basis of the analysis result, to extract the at least one multiscale feature.

14. The method of claim 10, wherein extracting the at least one of multiscale features of the input image includes extracting at least one of a complexity-based feature, a single-modal-based low-level feature, and a multimodal-based high-level feature according to a type and category of the input image.