CNN Features off-the-shelf: an Astounding Baseline for Recognition

Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the \overfeat network which was trained to perform object classification on ILSVRC13. We use features extracted from the \overfeat network as a generic image representation to tackle the diverse range of recognition tasks of object image classification, scene recognition, fine grained recognition, attribute detection and image retrieval applied to a diverse set of datasets. We selected these tasks and datasets as they gradually move further away from the original task and data the \overfeat network was trained to solve. Astonishingly, we report consistent superior results compared to the highly tuned state-of-the-art systems in all the visual classification tasks on various datasets. For instance retrieval it consistently outperforms low memory footprint methods except for sculptures dataset. The results are achieved using a linear SVM classifier (or $L2$ distance in case of retrieval) applied to a feature representation of size 4096 extracted from a layer in the net. The representations are further modified using simple augmentation techniques e.g. jittering. The results strongly suggest that features obtained from deep learning with convolutional nets should be the primary candidate in most visual recognition tasks.Comment: version 3 revisions: 1)Added results using feature processing and data augmentation 2)Referring to most recent efforts of using CNN for different visual recognition tasks 3) updated text/captio

Sparse representations and distance learning for attribute based category recognition

Abstract. While traditional approaches in object recognition require the specification of training examples from each class and the application of class specific classifiers, in real world situations, the immensity of the number of image classes makes this task daunting. A novel approach in object recognition is attribute based classification, where instead of training classifiers for the recognition of specific object class instances, classifiers are trained on attributes of the object images and these attributes are subsequently used for the object recognition. The attributes based paradigm offers significant advantages including the ability to train classifiers without any visual examples. We begin by discussing a scenario for object recognition on mobile devices where the attribute prediction and the attribute-to-class mapping are decoupled in order to meet the specific resource constraints of mobile systems. We next present two extensions on the attribute based classification paradigm by introducing alternative approaches in attribute prediction and attribute-to-class mapping. For the attribute prediction, we employ the recently proposed Sparse Representations Classification scheme that offers significant benefits compared to the previous SVM based approaches, such as increased accuracy and elimination of the training stage. For the attribute-to-class mapping, we employ a Distance Metric Learning algorithm that automatically infers the significance of each attribute instead of assuming uniform attribute importance. The benefits of the proposed extensions are validated through experimental results