Commodity style classification determination method and apparatus thereof

Abstract

The invention provides a commodity style classification determination method and an apparatus thereof. The method comprises the following steps of acquiring a commodity picture, and using a trained convolutional neural network to extract a characteristic vector of the commodity picture; calculating cluster density of the characteristic vector, and according to the cluster density, calculating a density distance between the characteristic vector and a first characteristic vector whose cluster density value is greater than a cluster density value of the characteristic vector; according to the cluster density of the characteristic vector and the density distance, determining an initial quantity and an initial center of a characteristic vector cluster; according to the initial quantity and the initial center of the cluster, carrying out characteristic vector clustering on the commodity picture, and acquiring a cluster result of a cluster stabilization condition satisfying setting; and according to the cluster result, determining commodity style classification. In the technical scheme provided in embodiments of the invention, an automatic, rapid, accurate and reliable classification basis is provided for a commodity style, accuracy and efficiency of commodity style classification are increased, and working strength of a worker is reduced.

Description

technical field [0001] The present application belongs to the technical field of image information data processing, and in particular relates to a method and device for determining commodity style classification. Background technique [0002] With the development of the Internet consumption era, consumers can choose their favorite products online, which greatly facilitates users to shop. For example, consumers can choose their favorite product types through the product pictures displayed by online merchants. [0003] Generally, when consumers purchase goods online, they are often influenced by various conceptual factors, such as brand, price, color, style, etc. These conceptual factors can generally be manually set by the merchant on the service operation platform. Among the many conceptual factors, some factors such as clothing brand, price, color, etc. are usually easy to define, and generally have relatively clear and standardized boundaries for distinction. For some ot...

AI Technical Summary

Problems solved by technology

In the actual product operation display, because the criteria for product style classification are often closely related to the background knowledge of industry operations, different operation platforms, different merchants and even different operators have uneven product style classifications, which often do not conform to reality. The classification of

For example, some style words are too large for consumers, corresponding to too many products, and cannot play the role of selecting products, while some style words are too small in scope, corresponding to too few products, and users have no room to choose

Moreover, due to the subjective style classification, the corresponding relationship between the classification results is often unclear, and there is a large overlapping space between various styles, which makes it difficult for consumers to distinguish and judge, and reduces the operation effect

At the same time, due to the wide variety of online products and the rapid growth of product information, sampling and manual classification of product styles will also consume a lot of labor and time costs, reducing the efficiency of classification

[0004] In the prior art, the use of artificial subjective judgment to determine the style classification of commodities will lead to inaccurate and reliable classification of commodity styles, and low classification efficiency

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