Multi scale orderless cross regions pooling of deep attributes for image retrieval
How to represent an image is an essential problem of the image retrieval task. To build a powerful image representation, a novel method named cross regions pooling (CRP) combining two key ingredients is proposed: (i) region proposals detected by objectness detection technique; (ii) deep attributes (DA) i.e. the outputs of the softmax layer of off the shelf convolutional neural network pre trained on a large scale dataset. The ultimate representation of an image is the aggregation (e.g. max pooling) of DA extracted from all the regions. In addition, a multi scale orderless pooling strategy considering layout of contexts of an image is proposed to integrate with CRP to improve the image representation. Experimental results on standard benchmarks demonstrate superiority of the proposed method over state of the arts.