The Research Of Object Detection Algorithm Combining Gradient With Saliency Features

Object detection is the task of retrieving location of interested objects from images,and object extraction is the task of extracting interested object from interested regions,thus,performing reliable object detection is becoming increasingly important in the fields of computer vision and robotics.However,the complexity of objects in the real world(e.g.the multitude of colors,textures,sizes,and cluttered or complex backgrounds)makes the design of algorithm becoming very difficult.In recent years,although object detection research has great strides,many problems and difficult points in theory research applications are still unsolved for object detection in single image.Complex features reduce the detection speed and single feature reduce the detection rate.Currently,there is no universal algorithm that can satisfy both requirements.Therefore,the research on object detection in single image is still important.To solve the above problems of target detection,we propose a combination of gradient and saliency features model(GSFM)for target detection.First of all,we handle for original image to get saliency image,which can highlight the interesting areas.Secondly,under guidance of the object detection model,we minimize the energy function by alternating minimization method to get optimal objectness feature and saliency feature.Then we get the only optimal region looked as object region according to regroup the regions corresponding to optimal features.Finally,the optimal region is obtained under the fixed different sizes of windows which are not adjustable to all objects in images,therefore,we implement multi-scale expansion algorithm on the optimal area and get a lot of windows which are substituted into the filter correction function,and then the maximum score,which is corresponding to the ultimate goal area,are obtained by compared to filter scores.Meanwhile,the proposed detection algorithm is further applied to automatically segmentation to overcome the defects of graph theory based segmentation method that it is difficult to set the initial window.Quantitative assessment verifies that the proposed target detection algorithm is effective,feasibleand robust.A large number of quantitative and visual comparative experiments illustrate the proposed method is not only effective but also better than the existing algorithms.Experiments of object extraction show that the proposed algorithm has strong applicability.