Fashion Learning adapts to Attributes’ Data
Fashion Learning adapts to Attributes’ Data The ancient Greek philosopher said that it is much easier to recognize the attributes of the concept than to understand it directly. Nowadays, his descendants employ this philosophy to improve objects understanding in each image at fine-grained level. This research direction is developed widely. These finegrained details are often called attributes. While local features are supposed to be better than global features, many handcrafted features like as SIFT, HOG, … are used to extracted visual features. However, the features are not appropriate to all attributes. In addition, to narrow semantic gap in fashion recognition and retrieval, local features are replaced by attribute vectors for each fashion image. Hence, attribute learning become a hot topic in many researches. http://www.aleeshainstitute.com/ In Computer Vision, attributes are used in face retrieval [3], fashion retrieval [2], violent’s attributes detection [17], crowd attributes ...