Penerapan Deskriptor Warna Dominan untuk Temu Kembali Citra Busana pada Peranti Bergerak

Nowadays, clothes with various designs and color combinations are available for purchasing through an online shop, which is mostly equipped with keyword-based item retrieval. Here, the object in the online database is retrieved based on the keyword inputted by the potential buyers. The keyword-based search may bring potential customers on difficulties to describe the clothes they want to buy. This paper presents a new searching approach, using an image instead of text, as the query into an online shop. This method is known as content-based image retrieval (CBIR).  Particularly, we focused on using color as the feature in our Muslimah clothes image retrieval. The dominant color descriptor (DCD) extracts the wardrobe's color. Then, image matching is accomplished by calculating the Euclidean distance between the query and image in the database, and the last step is to evaluate the performance of the DWD by calculating precision and recall. To determine the performance of the DCD in extracting color features, the DCD is compared with another color descriptor, that is dominant color correlogram descriptor (DCCD). The values of precision and recall of DCD ranged from 0.7 to 0.9 while the precision and recall of DCCD ranged from 0.7 to 0.8. These results showed that the DCD produce a superior performance compared to DCCD in retrieving a set of clothing image, either plain or patterned colored clothes.Nowadays, clothes with various designs and color combinations are available for purchasing through an online shop, which is mostly equipped with keyword-based item retrieval. Here, the object in the online database is retrieved based on the keyword inputted by the potential buyers. The keyword-based search may bring potential customers on difficulties to describe the clothes they want to buy. This paper presents a new searching approach, using an image instead of text, as the query into an online shop. This method is known as content-based image retrieval (CBIR).  Particularly, we focused on using color as the feature in our Muslimah clothes image retrieval. The dominant color descriptor (DCD) extracts the wardrobe's color. Then, image matching is accomplished by calculating the Euclidean distance between the query and image in the database, and the last step is to evaluate the performance of the DCD by calculating precision and recall. To determine the performance of the DCD in extracting color features, the DCD is compared with another color descriptor, that is dominant color correlogram descriptor (DCCD). The values of precision and recall of DCD ranged from 0.7 to 0.9 while the precision and recall of DCCD ranged from 0.7 to 0.8. These results showed that the DCD produces a superior performance compared to DCCD in retrieving a set of clothing image, either plain or patterned colored clothes

ファッションのための深層学習：服装の統一性評価と格付けおよび推薦

Tohoku University岡谷貴之課

Visual attribute discovery and analyses from Web data

Visual attributes are important for describing and understanding an object’s appearance. For an object classification or recognition task, an algorithm needs to infer the visual attributes of an object to compare, categorize or recognize the objects. In a zero-shot learning scenario, the algorithm depends on the visual attributes to describe an unknown object since the training samples are not available. Because different object categories usually share some common attributes (e.g. many animals have four legs, a tail and fur), the act of explicitly modeling attributes not only allows previously learnt attributes to be transferred to a novel category but also reduces the number of training samples for the new category which can be important when the number of training samples is limited. Even though larger numbers of visual attributes help the algorithm to better describe an image, they also require a larger set of training data. In the supervised scenario, data collection can be both a costly and time-consuming process. To mitigate the data collection costs, this dissertation exploits the weakly-supervised data from the Web in order to construct computational methodologies for the discovery of visual attributes, as well as an analysis across time and domains. This dissertation first presents an automatic approach to learning hundreds of visual attributes from the open-world vocabulary on the Web using a convolutional neural network. The proposed method tries to understand visual attributes in terms of perception inside deep neural networks. By focusing on the analysis of neural activations, the system can identify the degree to which an attribute can be visually perceptible and can localize the visual attributes in an image. Moreover, the approach exploits the layered structure of the deep model to determine the semantic depth of the attributes. Beyond visual attribute discovery, this dissertation explores how visual styles (i.e., attributes that correspond to multiple visual concepts) change across time. These are referred to as visual trends. To this goal, this dissertation introduces several deep neural networks for estimating when objects were made together with the analyses of the neural activations and their degree of entropy to gain insights into the deep network. To utilize the dating of the historical object frameworks in real-world applications, the dating frameworks are applied to analyze the influence of vintage fashion on runway collections, as well as to analyze the influence of fashion on runway collections and on street fashion. Finally, this dissertation introduces an approach to recognizing and transferring visual attributes across domains in a realistic manner. Given two input images from two different domains: 1) a shopping image, and 2) a scene image, this dissertation proposes a generative adversarial network for transferring the product pixels from the shopping image to the scene image such that: 1) the output image looks realistic and 2) the visual attributes of the product are preserved. In summary, this dissertation utilizes the weakly-supervised data from the Web for the purposes of visual attribute discovery and an analysis across time and domains. Beyond the novel computational methodology for each problem, this dissertation demonstrates that the proposed approaches can be applied to many real-world applications such as dating historical objects, visual trend prediction and analysis, cross-domain image label transfer, cross-domain pixel transfer for home decoration, among others.Doctor of Philosoph

Sistema automático de recomendación de outfits utilizando visión por computador y técnicas de aprendizaje de maquinas con un guardarropas personalizado

Decidir cómo combinar la ropa es un problema que afrontamos día a día, pocos tenemos la habilidad de saber qué va bien con qué pero a todos nos interesa vernos bien. La dificultad de abordar este problema es alta ya que para entrenar un modelo que pueda tomar decisiones de coordinación correctas es necesario previamente segmentar cada imagen, extraer características de esta y ensamblarlas. De acuerdo a esto se construye una base de datos con ayuda de una diseñadora de modas, en la cual se tiene el rating para miles de combinaciones de prendas..

Sistema automático de recomendación de outfits utilizando visión por computador y técnicas de aprendizaje de maquinas con un guardarropas personalizado

Decidir cómo combinar la ropa es un problema que afrontamos día a día, pocos tenemos la habilidad de saber qué va bien con qué pero a todos nos interesa vernos bien. La dificultad de abordar este problema es alta ya que para entrenar un modelo que pueda tomar decisiones de coordinación correctas es necesario previamente segmentar cada imagen, extraer características de esta y ensamblarlas. De acuerdo a esto se construye una base de datos con ayuda de una diseñadora de modas, en la cual se tiene el rating para miles de combinaciones de prendas..

Mobile Visual Clothing Search

We present a mobile visual clothing search system whereby a smart phone user can either choose a social networking photo or take a new photo of a person wearing clothing of interest and search for similar clothing in a retail database. From the query image, the person is detected, clothing is segmented, and clothing features are extracted and quantized. The information is sent from the phone client to a server, where the feature vector of the query image is used to retrieve similar clothing products from online databases. The phone's GPS location is used to re-rank results by retail store location. State of the art work focusses primarily on the recognition of a diverse range of clothing offline and pays little attention to practical applications. Evaluated on a challenging dataset, the system is relatively fast and achieves promising results

Mobile image parsing for visual clothing search, augmented reality mirror, and person identification

With the emergence and growing popularity of online social networks, depth sensors (such as Kinect), smart phones /tablets, wearable devices, and augmented reality (such as Google Glass and Google Cardboard), the way in which people interact with digital media has been completely transformed. Globally, the apparel market is expected to grow at a compound annual growth rate of 5 between 2012 and 2025. Due to the huge impact for ecommerce applications, there is a growing interest in methods for clothing retrieval and outfit recommendation, especially efficient ones suitable for mobile apps. To this end, we propose a practical and efficient method for mobile visual clothing search and implement it as a smart phone app that enables the user to capture a photo of clothing of interest with their smart phone and retrieve similar clothing products that are available at nearby retailers. Furthermore, we propose an extended method where soft biometric clothing attributes are combined with anthropometrics computed from depth data for person identification and surveillance applications. This addresses the increased terrorist threat in recent years that has driven the need for non-intrusive person identification that can operate at a distance without a subject’s knowledge or collaboration. We implement the method in a wearable mobile augmented reality application based on a smart phone with Google Cardboard in order to demonstrate how a security guard could have their vision augmented to automatically identify a suspect in their field of vision. Lastly, we consider that a significant proportion of photos shared online and via apps are selfies and of dressed people in general. Hence, it is important both for consumers and for industry that systems are developed to understand the visual content in the vast datasets of networked content to aid management and perform smart analysis. To this end, this dissertation introduces an efficient technique to segment clothing in photos and recognize clothing attributes. We demonstrate with respect to the emerging augmented reality field by implementing an augmented reality mirror app for mobile tablet devices that can segment a user’s clothing in real-time and enable them to realistically see themselves in the mirror wearing variations of the clothing with different colours or graphics rendered. Empirical results show promising segmentation, recognition, and augmented reality performance