A Discriminative Representation of Convolutional Features for Indoor Scene Recognition

Indoor scene recognition is a multi-faceted and challenging problem due to the diverse intra-class variations and the confusing inter-class similarities. This paper presents a novel approach which exploits rich mid-level convolutional features to categorize indoor scenes. Traditionally used convolutional features preserve the global spatial structure, which is a desirable property for general object recognition. However, we argue that this structuredness is not much helpful when we have large variations in scene layouts, e.g., in indoor scenes. We propose to transform the structured convolutional activations to another highly discriminative feature space. The representation in the transformed space not only incorporates the discriminative aspects of the target dataset, but it also encodes the features in terms of the general object categories that are present in indoor scenes. To this end, we introduce a new large-scale dataset of 1300 object categories which are commonly present in indoor scenes. Our proposed approach achieves a significant performance boost over previous state of the art approaches on five major scene classification datasets

Data Driven Approaches for Image & Video Understanding: from Traditional to Zero-shot Supervised Learning

In the present age of advanced computer vision, the necessity of (user-annotated) data is a key factor in image & video understanding. Recent success of deep learning on large scale data has only acted as a catalyst. There are certain problems that exist in this regard: 1) scarcity of (annotated) data, 2) need of expensive manual annotation, 3) problem of change in domain, 4) knowledge base not exhaustive. To make efficient learning systems, one has to be prepared to deal with such diverse set of problems. In terms of data availability, extensive manual annotation can be beneficial in obtaining category specific knowledge. Even then, learning efficient representation for the related task is challenging and requires special attention. On the other hand, when labelled data is scarce, learning category specific representation itself becomes challenging. In this work, I investigate data driven approaches that cater to traditional supervised learning setup as well as an extreme case of data scarcity where no data from test classes are available during training, known as zero-shot learning. First, I look into supervised learning setup with ample annotations and propose efficient dictionary learning technique for better learning of data representation for the task of action classification in images & videos. Then I propose robust mid-level feature representations for action videos that are equally effective in traditional supervised learning as well as zero-shot learning. Finally, I come up with novel approach that cater to zero-shot learning specifically. Thorough discussions followed by experimental validations establish the worth of these novel techniques in solving computer vision related tasks under varying data-dependent scenarios

Multi-scale Orderless Pooling of Deep Convolutional Activation Features

Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable scenes. To improve the invariance of CNN activations without degrading their discriminative power, this paper presents a simple but effective scheme called multi-scale orderless pooling (MOP-CNN). This scheme extracts CNN activations for local patches at multiple scale levels, performs orderless VLAD pooling of these activations at each level separately, and concatenates the result. The resulting MOP-CNN representation can be used as a generic feature for either supervised or unsupervised recognition tasks, from image classification to instance-level retrieval; it consistently outperforms global CNN activations without requiring any joint training of prediction layers for a particular target dataset. In absolute terms, it achieves state-of-the-art results on the challenging SUN397 and MIT Indoor Scenes classification datasets, and competitive results on ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets