Fisher discriminant dictionary learning-based warehouse goods identification method

Abstract

The invention relates to a Fisher discriminant dictionary learning-based warehouse goods identification method. The method comprises the following steps of: firstly dividing warehouse goods images acquired under different conditions into two parts: a training sample set and a test sample set; respectively preprocessing the two sample sets, rearranging pixel values and carrying out PCA dimensionality reduction; learning the training sample set through a Fisher criterion method to obtain a discriminant dictionary, and representing a test sample by using linear weighting of the discriminant dictionary; solving an L2 norm minimization problem by adoption of a least square method, so as to obtain a sparse representation matrix of the test sample under the discriminant dictionary; and finally realizing warehouse goods identification via ei formed by various types of reconstruction errors and sparse encoding coefficients. According to the method provided by the invention, the problems that the traditional identification method is greatly influenced by selected features, the identification process is relatively complicated and plenty of classification information is lost in the construction processes of common dictionaries are solved; and the correct and rapid identification of different goods can be realized, so that foundation is laid for the realization of intelligent warehouses.

Description

technical field [0001] The invention belongs to the technical field of warehouse item identification, in particular to a warehouse item identification method based on Fisher discriminant dictionary learning. Background technique [0002] Warehouses are places where materials are stored centrally, including various warehouses, warehouses, and freight yards that are stored by the state, collectives, or individuals. The important content of daily work is to serve the logistics and supply chain system, so there are high requirements for the management of warehouse goods. At present, many warehouse management tasks are done manually, requiring a lot of manpower to fill in various forms, vouchers, account books, cards and documents. Since the information is constantly changing with time, the warehouse data must be continuously summarized and counted according to different classifications, and many repeated registrations and transcriptions are often required. This manual manageme...

AI Technical Summary

Problems solved by technology

However, constructing a dictionary matrix directly with training samples is data redundancy. If there are too many training samples, a large number of calculations will become a difficult problem.

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