Latent Layout Analysis (LLA) is a novel unsupervised learning technique to discover objects in unseen images using a set of un-annotated training images. LLA defines a generative ...
We describe a scalable approach to 3D smooth object retrieval which searches for and localizes all the occurrences of a user outlined object in a dataset of images in real time. T...
In this paper, we describe a method of shape-based 3D model retrieval that employs a set of 3D, local, multi-scale features extracted from a voxel representation of a 3D model to ...
The objective of this paper is to study the existing methods for unsupervised object recognition and image categorization and propose a model that can learn directly from the outp...
Semantic-based storage and retrieval of multimedia data requires accurate annotation of the data. Annotation can be done either manually or automatically. The retrieval performance...
Omara Abdul Hamid, Muhammad Abdul Qadir, Nadeem If...