The current image retrieval algorithms usually extract features from the whole input image to conduct retrieval tasks.However

in many cases users focus on only a part of the image

i.e.object-of-interest.As a result

the features extracted from the image are partially effective.In other words

some of the features are ineffective and might have a negative impact on the retrieval process.To overcome this difficulty

an image retrieval scheme based on object-of-interest is proposed.By incorporating this retrieval scheme with the existing techniques in saliency detection

image segmentation

and feature extraction

an effective image retrieval algorithm is coded.First

the hierarchical saliency (HS) detection algorithm is adopted to analyze the user's object-of-interest

and the saliency-based image cut (SC) algorithm is employed to segment it from the input image.Then

we extract the hue

saturation

value (HSV) color features

the scale invariant feature transform (SIFT) local features and the convolutional neural network (CNN) semantic features of the object-of-interest.Finally

the similarity of object-of-interest between a query image and every database image is computed and the retrieval result is sorted accordingly.Simulation experimental results show that

when being used to cope with a retrieval task like what is this

the proposed algorithm is significantly better than the current image retrieval algorithms.