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Retrieval of Vehicle Images based on Color Space Fuzzy Quantification in Criminal Investigation

Volume 13, Number 6, October 2017 - Paper 4  - pp. 823-831
DOI: 10.23940/ijpe.17.06.p4.823831

Mingdi Hua,*, Mengbin Zhanga, Yilun Loub

aSchool of Communications and Information Engineering, Xi'an University of Posts & Telecommunications, Shaanxi, Xi'an ,710119, China
bSchool of Mathmatics and Information Science, Shaanxi Normal University, Shaanxi, Xi'an ,710119, China

(Submitted on July 25, 2017; Revised on August 30, 2017; Accepted on September 15, 2017)

(This paper was presented at the Third International Symposium on System and Software Reliability.)

Abstract:

The color and shape features provide more contributions for vehicle images in criminal investigation generally. A criminal investigation library that was used in studying criminal tools was set up, in which the number of the car images is more than one thousand. In this paper, the color of the car pictures was quantized by the non-uniform quantization, the triangular and the trapezoidal fuzzy membership degree functions respectively at the first step, and then the Euclidean distance and the weighted distance similarity measures methods are used at the second step, the last step is that the car images were retrieved by the above six algorithms in the criminal investigation library. The results show that the weighted distance similarity measure algorithm based trapezoidal membership degree is better than others; meanwhile the precision and recall are significantly higher than other else. The triangular fuzzy quantization algorithm is not very different from the non-uniform quantization algorithm, and even the precision and recall ratio is smaller than the latter. Under the same quantization condition, the weighted distance algorithm is slightly better than the Euclidean distance, but the difference is not great.

 

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