relativeattributesLearning relative attributes using parts

People Involved :Ramachandruni N Sandeep, Yashaswi Verma, C. V. Jawahar

Our aim is to learn relative attributes using local parts that are shared across categories. First, instead of using a global representation, we introduce a part-based representation combining a pair of images that specifically compares corresponding parts. Then, with each part we associate a locally adaptive “significance coefficient” that represents its discriminative ability with respect to a particular attribute. For each attribute, the significance-coefficients are learned simultaneously with a max-margin ranking model in an iterative manner. Compared to the baseline method , the new method is shown to achieve significant improvements in relative attribute prediction accuracy. Additionally, it is also shown to improve relative feedback based interactive image search.


imageAnnotation Image Annotation

People Involved :Yashaswi Verma, C V Jawahar

In many real-life scenarios, an object can be categorized into multiple categories. E.g., a newspaper column can be tagged as "political", "election", "democracy"; an image may contain "tiger", "grass", "river"; and so on. These are instances of multi-label classification, which deals with the task of associating multiple labels with single data. Automatic image annotation is a multi-label classification problem that aims at associating a set of text with an image that describes its semantics.