PATENT CLAIM ANALYSIS

Application Number: 15756193
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-08
Patent Classification: ["382", "190000"]

Abstract:
An image representation method and processing device based on local PCA whitening. A first mapping module maps words and characteristics to a high-dimension space. A principal component analysis module conducts principal component analysis in each corresponding word space, to obtain a projection matrix. A VLAD computation module computes a VLAD image representation vector; a second mapping module maps the VLAD image representation vector to the high-dimension space. A projection transformation module conducts projection transformation on the VLAD image representation vector obtained by means of projection. A normalization processing module conducts normalization on characteristics obtained by means of projection transformation, to obtain a final image representation vector. An obtained image representation vector is projected to a high-dimension space first, then projection transformation is conducted on a projection matrix computed in advance and vectors corresponding to words, to obtain a low-dimension vector; and in this way, the vectors corresponding to the words are consistent. The disclosed method and the processing device can obtain better robustness and higher performance.

Claim (Index 8):
The processing device according to  claim 6 , wherein when the PCA module conducts principal component analysis in each corresponding word space to obtain the projection matrix, the PCA module is configured to compute a transition matrix G i  with a formula below G i = 1 D \ue89e \u2211 j = 1 , k = 1 \ue89e \ue89e ( x j - c i ) \ue89e ( x k - c i ) T where c i  is the i-the word, x is the features assigned to the word, D is feature dimensionality, wherein the PCA module is further configured to perform eigen-decomposition on the matrix G i  with formulas below so as to obtained the eigenvalues eigval(G i ) and eigenvectors eigvect(G i ),\n (\u03bb 1 i ,\u03bb 2 i , . . . ,\u03bb D i )=eigval( G i ) \n ( u 1 i ,u 2 i , . . . ,u D i )=eigvect( G i ) \n wherein the PCA module is further configured to computing the projection matrix P t i  with a formula below,\n P t i =L t i U t i  \n where L t i = diag \ue8a0 ( 1 \u03bb 1 i + \u03f5 , 1 \u03bb 2 i + \u03f5 , \u2026 \ue89e , 1 \u03bb t i + \u03f5 ) , \ue89e U t i = [ u 1 i , u 2 i , \u2026 \ue89e , u t i ] \u03b5 and t are preset parameters.

Metadata:
- Claim Count in Document: 20.0
- Percentile: 88.0
- Lexical Diversity: 2.62319
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15063271', '15633679', '13084406', '14141612', '11418949']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3130156396119228
- 35 USC 102 Novelty (BERT): 0.5095683439298393
- Combined Prediction Score: 0.3326709100437145
- Mean Citation Score: 210.14636
- Max Citation Score: 232.61058
- Similarity Product: 152.17641045970916

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test