Image classification based on visual words
First Claim
1. A method performed by one or more processors configured with computer-executable instructions, the method comprising:
- extracting one or more classification image features from an image for classification;
quantifying, based on a similarity relationship between each classification image feature and each visual word in a pre-generated visual dictionary, each classification image feature by multiple visual words in the visual dictionary and determining a similarity coefficient between each classification image feature and each visual word after the quantifying;
determining, based on one or more similarity coefficients of each visual word corresponding to different classification image features, a weight of each visual word to establish a classification visual word histogram;
inputting the classification visual word histogram into an image classifier; and
using an output of the inputting to determine a classification of the image for classification,wherein the quantifying each classification image feature by multiple visual words in the visual dictionary and determining the similarity coefficient between each classification image feature and each visual word after the quantifying includes;
calculating, based on the similarity relationship between each classification image feature and the visual words in the pre-generated visual dictionary, a Euclidean distance between each classification image feature and each visual word,determining a smallest Euclidean distance among calculated Euclidean distances,determining, with respect to each classification image feature, one or more visual words of which Euclidean distances are within a preset times range of the smallest Euclidean distance as the visual words for quantification of the respective classification image feature, andcalculating, based on the Euclidean distance between the respective classification image feature and each of the visual words for quantification, the one or more similarity coefficients between the respective classification image feature and the visual words, the one or more similarity coefficients being calculated respectively as a percentage relationship of each of the one or more visual words for quantification,wherein the percentage relationship of a particular visual word for quantification is calculated by dividing the respective Euclidean distance of the particular visual word for quantification by a sum total of the Euclidean distances of the one or more visual words for quantification.
1 Assignment
0 Petitions
Accused Products
Abstract
The present disclosure introduces a method and an apparatus for classifying images. Classification image features of an image for classification are extracted. Based on a similarity relationship between each classification image feature and one or more visual words in a pre-generated visual dictionary, each classification image feature is quantified by multiple visual words in the visual dictionary and a similarity coefficient between each classification image feature and each of the visual words is determined. Based on the similarity coefficient of each visual word that corresponds to different classification image features, a weight of each visual word is determined to establish a classification visual word histogram of the image for classification. The classification visual word histogram is input into an image classifier that is trained by sample visual word histograms arising from multiple sample images. An output result is used to determine a classification of the image for classification.
24 Citations
15 Claims
-
1. A method performed by one or more processors configured with computer-executable instructions, the method comprising:
-
extracting one or more classification image features from an image for classification; quantifying, based on a similarity relationship between each classification image feature and each visual word in a pre-generated visual dictionary, each classification image feature by multiple visual words in the visual dictionary and determining a similarity coefficient between each classification image feature and each visual word after the quantifying; determining, based on one or more similarity coefficients of each visual word corresponding to different classification image features, a weight of each visual word to establish a classification visual word histogram; inputting the classification visual word histogram into an image classifier; and using an output of the inputting to determine a classification of the image for classification, wherein the quantifying each classification image feature by multiple visual words in the visual dictionary and determining the similarity coefficient between each classification image feature and each visual word after the quantifying includes; calculating, based on the similarity relationship between each classification image feature and the visual words in the pre-generated visual dictionary, a Euclidean distance between each classification image feature and each visual word, determining a smallest Euclidean distance among calculated Euclidean distances, determining, with respect to each classification image feature, one or more visual words of which Euclidean distances are within a preset times range of the smallest Euclidean distance as the visual words for quantification of the respective classification image feature, and calculating, based on the Euclidean distance between the respective classification image feature and each of the visual words for quantification, the one or more similarity coefficients between the respective classification image feature and the visual words, the one or more similarity coefficients being calculated respectively as a percentage relationship of each of the one or more visual words for quantification, wherein the percentage relationship of a particular visual word for quantification is calculated by dividing the respective Euclidean distance of the particular visual word for quantification by a sum total of the Euclidean distances of the one or more visual words for quantification. - View Dependent Claims (2, 3, 4, 5, 6, 7)
-
-
8. A system comprising:
-
one or more processors; and memory containing computer readable media storing one or more modules including instructions, which when executed by the processors, cause the modules to perform; extracting one or more classification image features from an image for classification, quantifying, based on a similarity relationship between each classification image feature and each visual word in a pre-generated visual dictionary, each classification image feature by multiple visual words in the visual dictionary and determining a similarity coefficient between each classification image feature and each visual word after the quantifying, determining, based on one or more similarity coefficients of each visual word corresponding to different classification image features, a weight of each visual word to establish a classification visual word histogram, and inputting the classification visual word histogram into an image classifier and using an output to determine a classification of the image for classification, wherein the quantifying includes; calculating, based on the similarity relationship between each classification image feature and the visual words in the pre-generated visual dictionary, a Euclidean distance between each classification image feature and each visual word, determining a smallest Euclidean distance among calculated Euclidean distances and, with respect to each classification image feature, determining one or more visual words of which Euclidean distances are within a preset times range of the smallest Euclidean distance as the visual words for quantification of the respective classification image feature, and calculating, based on the Euclidean distance between the respective classification image feature and each of the visual words for quantification, the one or more similarity coefficients between the respective classification image feature and the visual words, the one or more similarity coefficients being calculated respectively as a percentage relationship of each of the one or more visual words for quantification, and wherein the percentage relationship of a particular visual word for quantification is calculated by dividing the respective Euclidean distance of the particular visual word for quantification by a sum total of the Euclidean distances of the one or more visual words for quantification. - View Dependent Claims (9, 10, 11, 12)
-
-
13. One or more computer storage media including processor-executable instructions that, when executed by one or more processors, direct the one or more processors to perform a method comprising:
-
extracting one or more classification image features from an image for classification; quantifying, based on a similarity relationship between each classification image feature and each visual word in a pre-generated visual dictionary, each classification image feature by multiple visual words in the visual dictionary and determining a similarity coefficient between each classification image feature and each visual word after the quantifying; determining, based on one or more similarity coefficients of each visual word corresponding to different classification image features, a weight of each visual word to establish a classification visual word histogram; inputting the classification visual word histogram into an image classifier that is generated through training by sample visual word histograms from multiple sample images; and using an output of the inputting to determine a classification of the image for classification, wherein the quantifying each classification image feature by multiple visual words in the visual dictionary and determining the similarity coefficient between each classification image feature and each visual word after the quantifying includes; calculating, based on the similarity relationship between each classification image feature and the visual words in the pre-generated visual dictionary, a Euclidean distance between each classification image feature and each visual word, determining a smallest Euclidean distance among calculated Euclidean distances, determining, with respect to each classification image feature, one or more visual words of which Euclidean distances are within a preset times range of the smallest Euclidean distance as the visual words for quantification of the respective classification image feature, and calculating, based on the Euclidean distance between the respective classification image feature and each of the visual words for quantification, the one or more similarity coefficients between the respective classification image feature and the visual words, the one or more similarity coefficients being calculated respectively as a percentage relationship of each of the one or more visual words for quantification, wherein the percentage relationship of a particular visual word for quantification is calculated by dividing the respective Euclidean distance of the particular visual word for quantification by a sum total of the Euclidean distances of the one or more visual words for quantification. - View Dependent Claims (14, 15)
-
Specification