A Knowledge-Based System for Fashion Trend Forecasting

Transcription

1 A Knowledge-Based System for Fashion Trend Forecasting Paola Mello 1, Sergio Storari 2, and Bernardo Valli 3 1 DEIS - University of Bologna Viale Risorgimento, Bologna, Italy pmello@deis.unibo.it 2 ENDIF - University of Ferrara Via Saragat, Ferrara, Italy sergio.storari@unife.it 3 Faculty of Sociology - University of Urbino Carlo Bo Via Saffi, Urbino, Italy image@imagelab.w3n.it Abstract. In this paper, we show how artificial intelligence techniques can be applied for the forecasting of trends in the high creative domain of fashion. We describe a knowledge-based system that, starting from a set of keywords and pictures representing the concepts on which a fashion stylist chooses to base a new collection, is able to automatically create a trend forecast composed by the set of colors that better express these target concepts. In order to model the knowledge used by the system to forecast trends, we experimented Bayesian networks. This kind of model is learned from a dataset of past trends by using different algorithms. We show how Bayesian networks can be used to make the forecast and the experiments made in order to evaluate their performances. 1 Introduction There are sectors where the use of models is very important; in fact they can help in the comprehension of the surroundings and in the discovery of the natural laws in different sciences: Biology, Genetics, Chemistry, Physics. Among sectors where this knowledge modeling can be very interesting, we find the field of fashion, in particular the sphere of trends valuation. In this field, stylists and marketing experts study past trends and, according to their experience, formulate a forecasting on trends that will lead the next years collection. This study could prove to be very difficult and complicated both for experienced stylists and for small and medium companies that can only count on limited resources and experiences. In this paper, we describe our research activity aimed at investigating if artificial intelligence may be useful to support the stylist in the creative process which starts with the analysis of past fashion trends and ends with the forecasting of the new ones. In particular, we focused our attention on the creation of color proposals made by color stylists for the spun-yarn industries working in the fashion market. The creation of such proposals is highly influenced by the stylist creativity and experience and it is so

2 2 complex that only under widely simplifying hypothesis we could imagine to introduce partially creative elements combining Artificial Intelligence techniques as, for example, knowledge-based systems and data mining. Knowledge-based systems are able to solve problems in a limited domain but with performances similar to the ones of a human expert of the same domain. For this reason, a knowledge-based system seems suitable to support stylists and industry users in the creation of color proposal based on the analysis of past trends. One problem of such systems is knowledge acquisition because very often it can not be made manually, e.g. by making questions and interviews to the domain experts, in a easy and clear way. This is the case of the fashion domain as reported by Aarold Cohen [3]: I concluded that color is one of those things that we do not exactly think about; what I mean is that we have ways of manipulating it in the head, but the manipulation does not follow the more regular traffic of externalization into verbal constructs.... In order to overcome this limitation, data mining proposes several techniques to learn the knowledge base in a symbolic or sub-symbolic format from examples. Nowadays data mining techniques are widely used from a practical point of view to take advantage of the knowledge contained in databases. In the light of these considerations, in this article we propose an hybrid approach based on knowledge-based systems and data mining to support the creation of color proposal in fashion. The paper is organized as follows. The analysis of the process followed by a stylist to create a color proposal and the identification of where a support system may be useful is described in Section 2. Section 3 presents the data mining techniques that we evaluated as suitable for learning a knowledge base to be used in the support system. Section 4 and Section 5 describe respectively the experiments made to test the performance of the learned knowledge and how we use such knowledge to build a first prototype of the color proposal knowledge-based system. Conclusions follow in Section 6. 2 Analysis of the creative process Stylists use different creative processes to define a color/tissue proposal. Among them, in this paper we focus on the one used by an Italian stylist. This process, similar to the one used by other stylists worldwide, was analyzed in details by the ImageLab [6] staff, in order to better understand its phases and the more important information. From this analysis emerged that the process starts from the choice of some words and/or concepts taken by the stylist as the keywords of a new color proposal. As second step, the stylist associates to these words/concepts one or more pictures taken from various sources such as, for example, TV, journals, books and photos. In the third and last step, the stylist chooses more or less ten colors and some tissues that in his/her opinion better represent the words, concepts and pictures chosen in the previous steps. In this paper we propose to use a knowledge-based system to support the stylist in this last step of the creative process. In more details, the system should have the knowledge necessary to choose, among all the colors, the ones that better express the words, concepts and pictures chosen by the stylist. In the project we decided to elicit such knowledge from a collection of color proposals used in the past (named Proposal

3 3 Dataset P D). Each proposal contains: a set of words and concepts, the picture that has been associated to such words and concepts, and the set of chosen colors. Pictures have been digitalized in 16M colors. In order to decrease the complexity of the classification models, we decided to reduce the number of colors to 64, switching from 8bit to 2 bit for each RGB canal (the space has been subdivided in 64 cubes and all the colors belonging to a cube have been associated to the color in the cube center) 4. Given a picture, the frequency of each of the 64 colors is represented by the number of points that have a color which belongs in its color cube. Color frequencies have been discretized subdividing it by the highest color frequency in the picture (obtaining a relative frequency FR between 0 and 1) and assigning to it class N (not present) if F R = 0, B (low frequency) if 0 < F R 0.25, M (medium frequency) if 0.25 < F R 0.5, MA (medium high frequency) if 0.5 < F R 0.75 and A (high frequency) if F R > In the first experiments, these information have been digitally represented as follows: Words and concepts are modeled by boolean variables: a variable is True(False) if the word or concept it represents has(has not) been chosen in a color proposal; The 64 discretized color frequencies of the picture represented by the variables S6 1 S6 64 (N = not present, B = low, M = medium, MA = medium high, A = high); The chosen colors have been represented by 64 tonalities and considered as boolean variables C6 1 C6 64: a variable is True if the one or more colors of such tonality have been chosen in the proposal, otherwise False. P D contains 153 color proposals. Some of them are incomplete as they contain only words/concepts and chosen colors. In the experiments we used 29 words/concepts (Frivolousness, Sensuality, Nature, Metropolitan, Relax, etc.). 3 Data mining techniques for supporting color proposals Data mining concerns the automatic or semiautomatic exploration and analysis of data in order to discover meaningful, previously unknown, nontrivial and potentially useful knowledge. Data mining techniques can be subdivided in symbolic, if the extracted knowledge has a symbolic representation and can be easily understood by a human, and sub-simbolic, if the knowledge can only be understood by a computer. In our experiments, described in Section 4, we decided to focus on symbolic techniques for learning classifiers. We focus on classification because we see the problem of proposing or not a color C given the words/concepts W C and the picture colors S, chosen by the stylist, as a binary classification of the variable C in to propose and not to propose based on the values of the variables in W C and S. Moreover, we chosen the symbolic approach because, since the extracted knowledge is explicit, it is possible to explain to the user how a color has been proposed or not. Sub-symbolic techniques 4 The expert has considered this approximation acceptable because the suggestion of a particular limited range of colors is useful to guide him in the definition of the specific color to choose

4 4 does not offer such advantage but will be tested in future experiments to compare their performance w.r.t. the symbolic ones. Among the symbolic learning techniques, we tested Bayesian Networks [12]. A Bayesian network [12] is an appropriate method for dealing with uncertainty and probability, that are typical of real-life applications. A Bayesian network is a directed, acyclic graph (DAG) whose nodes represent domain variables and arcs represent probabilistic relations among them. In a Bayesian network each node is conditionally independent from any subset of nodes that are not its descendants, given its parents. By means of Bayesian networks, we can use information about the values of some variables to obtain probabilities for other variables. A probabilistic inference takes place once the probabilities functions of each node conditioned to just its parents are given. These are usually represented in a tabled form, named Conditional Probability Table (CPT). Given a training set of examples, learning a Bayesian network is the problem of finding the structure of the direct acyclic graph and the CPT associated with each node that best match (according to some scoring metric) the dataset. Optimality is evaluated with respect to a given scoring metric. A procedure for searching among possible structures is needed. The K2 algorithm [4] is a typical search and score method. It starts by assuming that a node has no parents, after which, in every step it adds incrementally the parent whose addition mostly increases the probability of the resulting structure. K2 stops adding parents to the nodes when the addition of a single parent cannot increase the probability of the network given the data. In the Experiments section, we will describe how we learned Bayesian networks from the available dataset by using the K2 algorithm and how we evaluated the classification performance of such networks. 4 Experiments This section describes the preparation of the datasets (Section 4.2), how we evaluate the performance of a learned Bayesian network (Section 4.4) and the performance results achieved by using different datasets and learning algorithms (Section 4.5). 4.1 Data mining and Bayesian network tools The Weka suite [14] (Waikato Environment for Knowledge Analysis), developed by the Waikato University of New Zeland, is a collection of machine learning algorithms. The suite, developed in Java, proposes a graphical user interface for easily accessing a lot of different algorithms, several pre-processing and post-processing methods, and some results visualization tools. In our research, we used the Weka Bayesian network learning algorithms, available in the classifiers group, and, in particular, the K2 algorithm implementation of Weka to learn a network and to analyze its performance. Moreover we saved the Bayesian network model in a Weka format. This model is then used by the knowledge-based system prototype, described in Section 5, to analyze a new case and classify a specific color as to propose or not to propose.

5 5 For the graphical visualization of the Bayesian network we used an opensource software named Genie [5]. 4.2 Dataset preparation In order to prepare the dataset for the data ming algorithms we followed several steps. In the first, we applied two filters: a filter to remove the variables which represent the colors in the picture S6 1 S6 64, obtaining a dataset containing only words/concepts and the chosen colors C6 1 C6 64 (the nopicture label is added to the dataset name if this filter is applied); a filter to remove the color proposals which contain missing values (the nomiss label is added to the dataset name if this filter is applied). Applying this filters we created two datasets: P D nomiss, used to evaluate the contribute that all the available information can give to the color classification; P D nopicture nomiss, used to evaluate the contribute that only the words/concepts can give to the color classification after the removal of the picture related information. As described in Section 3, the aim is to build a classifier for each color. Since we have 64 colors C6 1 C6 64, we learned 64 classifiers for the P D nomiss dataset and 64 for the P D nopicture nomiss dataset (for each classifier we have a dataset in which the class attribute is C6 i and the information used to make the classification are in the words/concepts and (eventually) the picture colors attributes). 4.3 Using the Bayesian network knowledge As previously described in Section 3, in order to use a Bayesian network for the classification of a target variable, it is necessary to assign a value to some or all the other domain variables. In our domain, the target variable is a color to propose or not to propose, while the other domain variables are the words/concepts (set as T if chosen, F otherwise) and the picture color frequencies. As an example, Figure 1 shows in Genie the network learned from the P D nomiss dataset for the C6 20 color. Fig. 1. Bayesian network model in Genie

6 6 Every node has associated an a priori probability for each admitted attribute value computed by mean of a statistical analysis of the dataset. As shown in Figure 1, the C6 20 target node has two possible values: T that corresponds to the to propose output and F that corresponds to the not to propose output. Moreover, this node has three parent nodes: natura, associated to the nature concept; energia, associated to energy concept; S6 9, associated to the frequency of the picture color number 9. Given the choice of a set of words/concepts and of a picture, if we want to use the Bayesian network for classifying the target color as to propose or not to propose we assign a value to the word/concept and the picture color attributes according to the user choice. Given value assignments, Bayesian network inference algorithms are capable to propagate the probabilities on the network and change the probability of the allowable values of the target color (to propose or not to propose). Figure 2 shows an example of this kind of probability propagation. Fig. 2. Inference in the Bayesian network model 4.4 Performance evaluation of a learned color classifier In order to evaluate the classification performance of a model, we created a confusion matrix and derived from it some performance indexes. Given a dataset of classified examples and a classification model, the confusion matrix is used to compare the original classifications w.r.t. the ones proposed by the model. In our experiments the dataset of classified examples is represent by the set of past color proposals, the classification model is a Bayesian network used to classify a specific color as to propose or not to propose and the confusion matrix appear as shown in Table 1. The cell names inside the confusion matrix have the following meaning: CNP = Correctly Not Proposed; ENP = Erroneously Not Proposed; EP = Erroneously Proposed; CP = Correctly Proposed. The cell values are used to compute four performance indexes: Accuracy = CP/(CP + EP ) where this index expresses the percentage of times a color has been correctly proposed over the total times it has been proposed; Sensitivity = CP/(CP + ENP )

7 7 Table 1. Confusion matrix Classification made by the Bayesian network Not to propose To propose Real choice Not to propose CNP EP (made by the stylist) To propose ENP CP where this index expresses the percentage of times a color has been correctly proposed over the total times it had to be proposed; Specificity = CNP/(CNP + EP ) where this index expresses the percentage of times a color has been correctly not proposed over the time it had not to be proposed; F alsealarmrate = 1 Specificity where this index expresses the percentage of times a color has been erroneously proposed over the time it had not to be proposed. The accuracy has been also evaluated as the number of correct classifications over the total number of cases: AccClass = (CNP + CP )/(CNP + CP + EP + ENP ). These indexes consider two different points of view: the first four are dedicated to stylists which prefer a model with high correctness of the to propose classification (an approach similar to the one proposed in [8]); The last index is dedicated to stylists which assign the same importance to the to propose and not to propose classifications, so they prefer a model with high performance for both of them. 4.5 Results The results shown in this section have been achieved by the K2 Bayesian network learning algorithms and evaluated by using the 10-fold cross validation. The dataset used in the experiments were P D nomiss (D1 for short) and P D nopicture nomiss (D2 for short). The K2 learning algorithm has been used with the option initasn aivebayes set as false (named k2 false) and as true (named k2 true). When this option is set as true (default), the initial network structure used in the learning is NaiveBayes like, with an arrow starting from the class attribute to each of the non-class attributes. When this option is set as false, the initial network has no arcs. The score function has been set as BAY ES. The results are shown in Table 2: since for each combination of dataset and algorithm we have learned 64 Bayesian networks (one for each color), in this table we show the average of the performance indexes achieved for all the colors (Accuracy (ACC), Sensitivity (SENS), Specificity (SPEC), False Alarm rate (FA) and AccClass (ACL)). Table 2. Results of the K2 false and K2 true algorithms K2 false on D1 K2 false on D2 K2 true on D1 K2 true on D2 ACC SENS SPEC FA ACL

8 8 Analyzing these results, we observe that the Bayesian networks learned from the dataset which contains only the words/concepts chosen by the stylist (D2) achieve worst classification performances than the ones learned from the dataset which contains also the colors of the pictures (D1). Picture information are then effectively useful to perform good colors forecasting. Moreover we observe that the performance achieved by k2 f alse are better than the k2 true one. Following these considerations the classification models used in the knowledge-based system, described in Section 5, are the ones learned by k2 false on D1. Fig. 3. Graphic user interface of the prototype 5 First prototype Following the experiment results, described in Section 4.5, we choose the 64 Byesian networks learned by k2 false on D1 as knowledge to use in the first prototype of the color proposal supporting system. The interaction between the user and the knowledge base is managed by the graphic user interface (GUI) shown in Figure 3 that allows: the definition of a new color proposal creation request; the consultation of the knowledge base to create a color proposal; the visualization of the color proposal. The definition of a new color proposal request is made by the user selecting: in the checkboxs on left side of the GUI the words/concepts on which the proposal should be

9 9 focused; in the top of the GUI the file which specifies the frequencies of the color inside the picture that the user has associated to the words/concepts previously chosen. This choice is made by the user considering, for example, his/her experience, current trends and personal taste. When the Evaluate button is pressed, the system creates the new color proposal following several steps. In the first, it creates the new color proposal request in the format described in Section 2 (extraction of other attributes from the pictures will be considered in future works). This request is then sent to the 64 Bayesian networks that, following the inference methodology described in Section 4.3, classify each color as to propose and not to propose. The inference engine that interprets the Bayesian networks to make the classification is the one implemented in the weka.classif iers.bayesn et class of Weka [14]. The set of colors proposed by the system is shown in the right side of the GUI. 6 Conclusions and Discussion In this paper we presented a knowledge-based system that is able to create automatically the forecasting of trends in fashion represented by the colors to use in a new collection. In order to reach this goal, at first we have analyzed the creative process followed by a stylist, identifying a set of words/concepts and a picture as the most significant information used by him/her to make the forecast. As second step we used data mining techniques for eliciting, from a dataset of past color proposals, the knowledge necessary to create a new proposal. Finally we developed a prototype of a knowledge-based system that effectively uses the elicited knowledge to support the color proposal creation. The stylist that has supplied the starting dataset has found the elicited knowledge and the performance results very interesting. In particular in her opinion it is valuable the ability of the Bayesian network to make explicit the words/concepts and picture colors most influencing each color choice. Moreover, she confirmed that many of such probabilistic models are effectively consistent with her choices. She considered these models useful also for other reasons: they can be used by pret-a-porter stylists to analyze successful fashion collections in order to find the most appropriated color combinations; they can be used by high fashion stylists to differentiate their collection from the high distribution fashion collections proposing something original. Beside Bayesian networks, Decision tree [13] learning techniques have also been tested on the dataset of color proposals. In the experiments, described in [10], Bayesian networks have achieved the best predictive performances. Among the necessary future steps we consider important to increment the number of color proposals in the dataset used to elicit the system knowledge base and to make a deep analysis of the informative value of pictures. Despite the achieved results, we are aware that the application domain is very complex and that the combination of knowledge-based systems and data mining only under widely simplifying hypotheses could be considered a real creative process. The aim of creating such creative systems is the goal of a research area of artificial intelligence named Computational Creativity [11][2]. This high interdisciplinary area

10 10 (computer science, notional psychology, philosophy and art) does not fall within the areas that have the intention to suggest problems solution but it is part of the areas that aim at the generation of manufactures of value: different artificial intelligence techniques are used to produce these manufactures, to value them through an utility function and to select some of them. Systems based on creativity rules have been proposed in different application fields: in melodies, for the improvisation of jazz music ( [7]Philip Johnson - Laird - University of Princeton); in art, for the creation of paintings (AARON [1] by Harold Cohen University of California); in theorems, for the discovery of mathematical rules (AM [9] Douglas Lenat Stanford University). In spite of all these applications, as we know, computational creativity has never been applied to fashion and in particular to the forecasting of trends for color selection. For this reason we consider the work described in this paper as a first step on the development a real creative system for fashion. Acknowledgments This research activity has been partially funded by the Italian Cofin 2003 project (prot ). We would like to thank Ornella Bignami (Elementi Moda s.r.l.), Alessandro Di Caro (ImageLab), Erika D Amico (ImageLab), Emanuela Ciuffoli (ImageLab), Enrico Betti (ImageLab), Elena Giliberti (ImageLab), Beatrice Universo (ImageLab) and Enrico Munari (University of Ferrara) for their precious help in the research. References 1. Aaron. Home Page: 2. M. Boden. Agents and Creativity. Communications of the ACM, H. Cohen. The further exploits of aaron, painter. Stanford Humanities Review, 4(2), G. Cooper and E. Herskovits. A bayesian method for the induction of probabilistic networks from data. Machine Learning, 9: , Genie. Home Page: 6. Imagelab, universita di urbino. Home Page: 7. P. Johnson-Laird. Jazz Improvisation: A theory at the computational level. TRepresenting Musical Structure. Academic Press, London, N. Lavrac. Machine learning for data mining in medicine. In W. Horn, Y. Shahar, G. Lindberg, S. Andreassen, and J. Wyatt, editors, Proc. Artificial intelligence in medicine (AIMDM), number 1620 in LNAI, pages 47 64, Berlin, Springer Verlag. 9. Douglas B. Lenat and John Seely Brown. Why am and eurisko appear to work. Artif. Intell., 23(3): , P. Mello, S. Storari, and B. Valli. Application of machine learning techniques for the forecasting of fashion trends. Intelligenza Artificiale, To appear. 11. M. Minsky. The Society of Mind. Simon and Schster, New York, J. Pearl and S. Russell. Bayesian networks. In M.A. Arbib, editor, The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge, MA, R. Quinlan. Induction of decision trees. Machine Learning, vol.1 n.1:81 106, I.H. Witten and F. Eibe. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, 2005.